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Organizational decision-making that leverages the collective wisdom and knowledge of multiple individuals is ubiquitous in management practice, occurring in settings such as top management teams, corporate boards, and the teams and groups that pervade modern organizations. Decision-making structures employed by organizations shape the effectiveness of knowledge aggregation. We argue that decision-making structures play a second crucial role in that they shape the learning of individuals that participate in organizational decision-making. In organizational decision making, individuals do not engage in learning-by-doing, but rather, in what we call learning-by-participating, which is distinct in that individuals learn by receiving feedback not on their own choices, but rather on the choice made by the organization. We examine how learning-by-participating influences the efficacy of aggregation and learning across alternative decision-making structures and group sizes. Our central insight is that learning-by-participating leads to an aggregation-learning tradeoff in which structures that are effective in aggregating information can be ineffective in fostering individual learning. We discuss implications for research on organizations in the areas of learning, microfoundations, teams, and crowds.
The Aggregation-Learning Tradeoff
Henning Piezunka
Vikas A. Aggarwal
Hart E. Posen
University of Wisconsin-Madison
Forthcoming in Organization Science
March 18, 2021
Abstract. Organizational decision-making that leverages the collective wisdom and knowledge of multiple
individuals is ubiquitous in management practice, occurring in settings such as top management teams,
corporate boards, and the teams and groups that pervade modern organizations. Decision-making structures
employed by organizations shape the effectiveness of knowledge aggregation. We argue that decision-
making structures play a second crucial role in that they shape the learning of individuals that participate
in organizational decision-making. In organizational decision making, individuals do not engage in
learning-by-doing, but rather, in what we call learning-by-participating, which is distinct in that individuals
learn by receiving feedback not on their own choices, but rather on the choice made by the organization.
We examine how learning-by-participating influences the efficacy of aggregation and learning across
alternative decision-making structures and group sizes. Our central insight is that learning-by-participating
leads to an aggregation-learning tradeoff in which structures that are effective in aggregating information
can be ineffective in fostering individual learning. We discuss implications for research on organizations in
the areas of learning, microfoundations, teams, and crowds.
The authors thank Felipe Csaszar, Javier Gimeno, Henrich Greve, Thorbjørn Knudsen, Stephen Mezias, Phanish Puranam,
Mooweon Rhee, Violina Rindova, two anonymous reviewers, seminar participants at the Max Planck Institute, University of
Pennsylvania, University of Toronto, Seoul National University and Yonsei University, and conference participants at the Strategic
Management Society special conference in Frankfurt, for valuable feedback and suggestions. The authors gratefully acknowledge
support from the INSEAD Alumni Fund. All authors contributed equally.
The Aggregation-Learning Tradeoff
Organizational decision-making that leverages the collective wisdom and knowledge of multiple
individuals is ubiquitous in management practice, occurring in settings such as top management teams,
corporate boards, and the teams and groups that pervade modern organizations. Numerous threads of the
organization science literature have consequently sought to study how individual knowledge aggregates to
the level of the organization. This work spans a range of topics, including organization design (e.g.,
Knudsen and Levinthal 2007, Christensen and Knudsen 2010, Csaszar and Eggers 2013), information
processing (e.g., Turco, 2016, Mack and Szulanski 2017, Joseph and Gaba 2020), crowds (Mannes, Soll
and Larrick 2014, Mollick and Nanda 2016, Becker 2017, Felin, Lakhani and Tushman 2017, Keuschnigg
and Ganser 2017), and collective intelligence (e.g., Page 2007, Woolley et al. 2010, Bernstein, Shore and
Lazar 2018). Uniting much of this work is the recognition that the decision-making structures organizations
employ, such as plurality voting or delegation, shape the effectiveness of knowledge aggregation.
In this paper we argue that decision-making structures play a critical role beyond just shaping
knowledge aggregationthey also shape the learning of individuals that participate in organizational
decision-making. If decision-making structures that better aggregate individuals’ knowledge are also better
at facilitating individual learning, distinguishing between these dual outcomes of knowledge aggregation
and individual learning would be unnecessary. There is, however, no ex ante reason to believe that these
two outcomes operate in concert. Indeed, it may well be the case that decision-making structures that more
effectively aggregate individuals’ knowledge may ultimately be less effective at facilitating the learning
trajectory of individuals within the organization. Were this the case, it could give rise to the possibility of
an aggregation-learning tradeoff.
To explore this potential tradeoff, we start with the observation that learning by individuals in the
context of an organization differs from the traditional learning-by-doing. This is because in a traditional
learning-by-doing situation, an individual makes a decision alone by selecting from a menu of alternatives
and garnering performance feedback on that alternative (e.g., Cyert and March 1963, Denrell and March
2001). By contrast, in an organizational context, an individual participates in the organization’s decision-
The Aggregation-Learning Tradeoff
making by voicing her preferred alternative to the group, which then chooses an alternative based on the
particular decision-making structure employed by the organization. We refer to this process of individual
decision-making in an organizational context as learning-by-participating. When individuals learn-by-
participating, the alternative chosen by the organization does not necessarily correspond to the alternative
favored by the individual; and as a consequence, the individual receives performance feedback on the
organization’s choice (and not necessarily on their own).
Learning-by-participating forces us to grapple with the recognition that decision-making structures
may vary not just with respect to how effectively they tap into and aggregate individual knowledge (e.g.,
Sah and Stiglitz 1986), but also that they may vary with respect to how effectively they shape individuals’
learning. In order to understand the implications of learning-by-participating for the link between decision-
making structures, aggregation, and learning, we proceed in two-stages. First, we develop a theoretically
grounded conceptual framework to characterize the organizational decision-making structures used to
aggregate individuals' knowledge to organizational decisions. Second, we employ a computational model
of organizational decision making under uncertainty to compare organizations that vary with respect to
different decision-making structures. In our analyses we consider not only the structure of decision-making
itself, but also the role of organizational size (number of organizational members). Our model extends the
canonical single-agent model of learning under uncertainty, the multi-armed Bandit (e.g., Posen and
Levinthal 2012, Stieglitz, Knudsen and Becker 2016, Puranam and Swamy 2016, Laureiro‐Martinez,
Brusoni, Tata and Zollo 2019), to a multi-agent setting (e.g., Aggarwal, Posen and Workiewicz 2017).
The central insight of this paper is that learning-by-participating leads to an aggregation-learning
tradeoff: the efficacy of information aggregation and the extent of individual learning are inversely related.
In other words, organizations that are more effective in aggregating information are less effective in
fostering individuals’ learning. We find that this tradeoff between information aggregation and individual
learning exists both across different decision-making structures as well as across different organizational
sizes (i.e., number of individuals participating in decision-making). Since organizational performance over
the longer-term depends also on how much its individual members learn, the tradeoff between effective
The Aggregation-Learning Tradeoff
aggregation and individual learning translates into a short-term versus long-term tradeoff with respect to
organizational performance. Organizations need to decide whether to adopt a decision-making structure
and size that is effective in aggregating and performant in the short-term, or one that is effective in fostering
the learning of its members and performance in the long-term.
We find that at the core of this tradeoff is the role of organizational contrariansindividuals that
prefer alternatives other than that chosen by the organization. Different organizational decision-making
structures draw on and shape the knowledge of contrarians in different ways. Decision-making structures
that are most effective in aggregating individuals’ knowledge tend to marginalize contrarians. This
marginalization process is akin to how, in the wisdom-of-crowds, incorrect estimates cancel each other out
(e.g., in Galton’s famous ox experiment [Galton, 1907]). By contrast, decision-making structures that are
most effective in fostering individual learning leverage contrarians rather than marginalizing them: they
allow contrarians to learn and allow other organizational members to learn from contrarians.
2.1. Learning-by-participating: Bridging experiential learning and information aggregation
Our research bridges two research streams in the Carnegie school tradition that have developed mostly
independently of one another: experiential learning and information aggregation.
The first stream, experiential learning, focuses on how performance feedback shapes beliefs about
the merits of an agent’s choice alternatives and subsequent actions (e.g., Levinthal and March 1981, Miner
and Mezias, 1996, Greve 2003, Reagans, Argote and Brooks 2005, Denrell and Le Mens 2007, Argote and
Miron-Spektor 2011, KC, Staats and Gino 2013, Jaspersen and Peter 2017, Sengul and Obloj 2017, Clough
and Piezunka 2020). An individual learns-by-doing as she makes a choice, receives feedback on that choice,
and updates her beliefs about the merits of the alternatives based on feedback. A basic assumption, which
we relax in this paper, is that the agent receives feedback on the alternative she has chosen.
In parallel with this work on experiential learning, a second pillar focuses on the topic of
information aggregation in organizations. This work has its intellectual roots in Simon’s (1947) view of
the organization as an information processing system. Scholars in this stream have built on Sah and Stiglitz
(1986) to examine the link between structures of decision making and organizational outcomes (Knudsen
The Aggregation-Learning Tradeoff
and Levinthal 2007, Christensen and Knudsen 2010, Csaszar 2013), as well as the ways in which structure
shapes how individual-level knowledge aggregates to shape organization-level outcomes (e.g., March 1991,
Argote, McEvily and Reagans, 2003, Reitzig and Sorenson 2013, Reitzig and Maciejovsky 2015, Criscuolo
et al. 2017, Keum and See 2017, Piezunka and Dahlander 2015, 2019, Davis and Aggarwal 2020, Aggarwal,
Hsu and Wu, 2020).
These two pillarsexperiential learning and information aggregationhave evolved somewhat
independently. The information aggregation literature has had as its primary focus the question of how
organizations can tap into and aggregate the knowledge of individuals, with the (often implicit) assumption
that individuals’ capacity to learn is limited.
With a few exceptions where scholars have studied learning
among interdependent individuals (Knudsen and Srikanth, 2014; Aggarwal et al., 2017, Puranam and
Swamy 2016), the literature on experiential learning has generally tended to abstract away from
organizations’ decision-making structures, instead conceptualizing organizations as unitary actors (e.g.,
Levinthal 1997, Posen and Levinthal 2012).
Taken together, these two streams of literature collectively point to the theoretical merit of
studying how organizational decision-making structures shape the trajectory of experiential learning by
individuals, and whether this then leads to an aggregation-learning tradeoff.
By examining this issue, we
complement prior work at the intersection of individual and experiential learning in the context of issues
such as social learning (e.g., March 1991, Denrell and Le Mens 2007, Fang et al. 2010) and learning with
task decomposition (e.g., Knudsen and Srikanth 2014, Puranam and Swamy 2016, Aggarwal et al. 2017).
Our point of departure is the recognition that even in the absence of social learning and task decomposition,
there is a fundamental, but as yet unexplored, dynamic relation between the design of organizational
decision-making structures, the efficacy of information aggregation, and the extent of individual learning.
This assumption can be thought of in a number of different ways. It may reflect an organization making a series of independent
decisions with no learning possible across them. It may also reflect the timescale of learning in the sense that in some settings,
individuals may reach steady-state knowledge rather quickly, such that further experience does not meaningfully change their
Of course, the idea that decision-making structures shape the knowledge of organizational members over time is itself not new
(e.g., Coleman 1990, March 1991). Work in the Sah and Stiglitz tradition has certainly recognized that the choice of decision-
making structure may shape and complicate individual learning (e.g., Sah and Stiglitz 1986, Knudsen and Levinthal 2007,
Christensen and Knudsen 2009, Knudsen, Marchiori and Warglien, 2018).
The Aggregation-Learning Tradeoff
2.2. Conceptualizing different decision-making structures in organizations
We consider four organizational decision-making structures that reflect the broad spectrum of structures
discussed in prior work and used in practice. In considering the behavioral plausibility of these decision-
making structures, we note Csaszar and Eggers’ (2013: 2262) observation that while “there are an unlimited
number of potential decision-making structures to study,” the ultimate choice is driven by prevalence in
real-world organizations.
We consider four decision-making structures that are well-known in practicein boards,
committees, teams, and political democracies. Plurality voting is the structure in which the alternative that
gathers the largest number of participants’ votes becomes the organizational choice. It is used in many
social and organizational situations (e.g., most US elections or voting by boards of directors). Two-stage
voting is the structure in which in the first stage the two alternatives gathering the most votes are selected,
and then in a second stage participants vote only on these two, with the alternative in the second stage
garnering the most votes becoming the organizational choice. This structure is common in organizational
settings in which a large set of alternatives is whittled down by first stage voting to create a shortlist, and a
subsequent vote is made to select from that list. It is also common in electoral systems (see e.g., Fishburn
and Gehrlein 1976). Average-beliefs is the structure in which the beliefs of all participants regarding the
value of each alternative are averaged. This creates a notional individual that makes decisions on the basis
of the average-beliefs across alternatives (Hastie and Kameda 2005, Csaszar and Eggers 2013). Each
member of the team rates each alternative, averages for each alternative across individuals are calculated,
and the alternative with the highest average is then the organizational choice. Finally, rotating dictatorship
is a form of hierarchical delegation of authority to a single individual (Bodily 1979) in which decisions are
delegated to randomly selected individuals over time (Gibbard 1977, Barberà, Sonnenschein, and Zhou
1991, Chatterji, Sen, and Zeng 2014). While delegation is common, randomness in delegation is less
common, although it was used in ancient Athenian democracy (Ober 2009) and recently used in the Belgian
The Aggregation-Learning Tradeoff
political system.
It has also been observed in the context of inter-firm collaborations (Davis and Eisenhardt
To understand the implications of learning-by-participating, we require a theoretically grounded
conceptual framework to characterize these structures. We use the literature on social choice as an
anchoring point to develop this framework. We think of decision-making structures as a mapping from
individual-level beliefs to an organization-level choice. The basic structure articulated by Arrow (1951),
which continues to be the basis of much research, is that inputs are messages of judges or voters and [the]
outputs are the jury or electoral decisions” (Balinski and Laraki 2007: 8720) (emphasis added). Our
conceptual framework distinguishes among the input side of the decision-making, with decision-making
structures differing along two dimensions, as depicted in Figure 1.
[Insert Figure 1 here]
The first dimension of the input side of our conceptual framework is that each individual’s beliefs
are transformed into “messages” that are transmitted to the organization (Balinski and Laraki 2007), as
depicted on the x-axis in Figure 1. In our setting, a message is the information of individuals’ beliefs (i.e.,
utilities, preferences, etc.) about the merits of the alternatives. For example, a message can take the form of
a ballot in a group decision-making setting. Depending on the structure, it may include the name of the
alternative for which the individual votes, or the full list of the individual's beliefs across the alternatives.
We consider only monotonic (i.e., rank preserving) transformations. The transformation can be minimal
(indeed, none), as in the case of the transmission of individuals’ raw (untransformed) beliefs, which is the
basis of the average-beliefs decision-making structure. A major transformation, as in the plurality voting
decision-making structure, is one in which an individual’s highest belief across alternatives is coded as 1
and all others as 0. This transformation is lossy in an information theory sense. In between these two
extremes are different forms of rank ordering transformations (Fishburn 1974). For instance, the two-stage
Plurality and two-stage voting follow naturally from work on polyarchy and hierarchy (Sah and Stiglitz 1986, Christensen and
Knudsen 2010). In hierarchy, all participants must vote in favor, while only one needs do so in a polyarchy. The conditions under
which either of these structures is optimal, even in the simple no-learning case, are restrictive (Ben-Yashar and Nitzan, 1997). This
has given rise to interest in intermediate structures (i.e., G*) in which the decision is determined by a threshold that is some subset
of votes (e.g., plurality voting).
Despite these precedents, rotating dictatorship is probably the most novel organizational structure, as the theoretical debate has
typically not examined the random allocation of decision rights (Puranam, Alexy and Reitzig, 2013). In typical (and typically
studied) organizations, decision rights are allocated following hierarchical or lateral principles (e.g., O’Mahony and Ferraro, 2007;
Dahlander and O’Mahony 2011; Klapper, Dahlander and Piezunka 2021).
The Aggregation-Learning Tradeoff
voting decision-making structure (sometimes called ‘majority runoff’ or ‘double plurality’), uses a binary
transformation of beliefs in the first round, and another binary transformation in the second round on the
two alternatives not eliminated (Fishburn and Gehrlein 1976).
The second dimension of the input side of our conceptual framework is that each individual’s
message may, or may not, be included as an input into the organizational decision. We depict this on the y-
axis in Figure 1. Individuals’ messages transmitted to the organization may be assigned different weights.
When each individual is weighted the same, we describe this as balanced weighting. When the weights
differ across individuals, inclusion is unbalanced. While there is, of course, a large set of potential weighting
systems, we consider only decision-making structures in which weights are binary. One can think of
individual inclusion in organizational decision-making as delegation of the decision to a subcommittee of
individuals that includes or excludes certain individuals from the organizational decision (Csaszar and
Eggers 2013).
The output side of decision-making structures is the aggregation of the messages of individuals
included in decision-making. We consider an output function that is common to all four decision-making
structures we study. We assume the organization sums each individual’s message on each alternative,
adjusted by each individual’s weight, and then chooses the alternative that has the maximum score.
3.1. Model overview
We envision a setting in which an organization chooses among a fixed set of independent alternatives over
time. The challenge faced by the organization is that the alternatives are of uncertain merit.
Plurality voting, average-beliefs, and two-stage voting are settings in which individuals’ beliefs are given equal weighting that
does not change over time. That is, in each period, all individuals’ messages are included — to be aggregated into an organizational
decision. Individual inclusion in decision-making is interesting in settings with repeated decision making with inclusion weights
that change over time. The simplest version of time varying inclusion is that of rotating dictatorship, in which one individual is
randomly selected in each period and given a weight of 1, while all others are given weights of 0. Information aggregation is only
indirect in that it functions intertemporally, mediated by individual learning-by-participating. In each period, only one individual’s
messages are considered, but over time, every individual’s messages will be included in organizational decisions. More complex
inclusion settings, which may be interesting to explore in future research, feature endogenously varying fractional weights that
evolve over time, perhaps due to emergent differences in power, status, or ability (e.g., Ben-Yashar and Nitzan 1997).
In the case of the two-stage decision-making structure, the message transformation and output procedure occurs twice, the first
time including all alternatives, and the second time including only the top two vote getters.
Of course, this is not the only challenge that may be faced by organizational decision making. For instance, one might consider
the challenge of complexity, rather than uncertainty, as in the single agent model of Levinthal (1997) or the organizational models
of Siggelkow and Rivkin (2003).
The Aggregation-Learning Tradeoff
feedback from selecting an alternative is imperfect and noisy, and as such, one learns from experience with
a particular alternative only slowly and imperfectly. Each individual in the organization has beliefs about
the merits of the alternatives that evolve over time and voices those beliefs as an input to organizational
decision-making across time.
Our model builds on the single agent (unitary actor) multi-arm bandit model, the canonical
representation of learning under uncertainty (Sutton and Barto 1998, Denrell and March 2001, March 2010,
Posen and Levinthal 2012, Baumann, 2015, Puranam and Swamy 2016, Stieglitz, Knudsen and Becker
2016, Laureiro‐Martinez, Brusoni, Tata and Zollo 2019). There are two key individual behavioral
assumptions in the model: reinforcement learning from performance feedback, and the opportunity to
explore across the set of choice alternatives. The literature suggests that the bandit model provides a good
representation of the psychological processes underlying individual learning under uncertainty. As Puranam
et al. (2015: 342) note, the bandit is appropriate in settings where choices involve “discrete alternatives
with uncertain and unknown outcomes,” together with subjective beliefs about these alternatives that evolve
over time through reinforcement learning. In the management literature, the bandit has been employed to
model responses to environmental turbulence (Posen and Levinthal 2012), the formation of organizational
routines (Aggarwal et al. 2017), risk-taking (Denrell and March 2001), and agency considerations (Lee and
Puranam 2016). Not all decision-making contexts are characterized by this model. For example, work on
learning via adaptive aspiration reflects a different decision-making context (Levinthal and March 1981),
as does work on learning under complexity, particularly characterized by the NK model (Levinthal 1997,
Rivkin and Siggelkow 2003, Adner, Csaszar and Zemsky 2014, Rahmandad 2019).
The single-agent model is an analogy to a slot machine with multiple “arms.” Each arm reflects a
choice alternative that has a fixed probability of yielding a reward (success) in a given period. The reward
probabilities associated with the arms are, ex ante, unknown to individuals. Individuals have beliefs (priors)
about the merits of each of the arms. They seek to enhance their decision-making by learning about the
merits of the arms: sampling arms and receiving performance feedback in the form of success or fail
outcomes. The goal for an individual is to identify a high-performing arm, just as a gambler may seek to
learn about the various slot machines in a casino in order to find a high-payoff machine.
The Aggregation-Learning Tradeoff
We extend the standard single-individual bandit model to consider an organization consisting of
multiple individuals. In the organizational setting, individuals’ beliefs about the merits of the choice
alternatives are the inputs to organizational decisions. Individuals’ beliefs are aggregated into
organizational decisions based on the specifics of the decision-making structure in use (per the details
outlined in Section 2.2 and Figure 1). Individuals learn-by-participating. That is, all individuals in the
organization get feedback on the organization’s choice, and they each update their beliefs on that basis.
3.2. Individual beliefs, organizational choices, and individual learning
The task environment faced by the individuals in an organization consists of N choice alternatives. Each
alternative is Bernoulli in that the reward to choosing an alternative is either success (reward = 1) or failure
(reward = 0), with probability  . These probabilities, which are ex ante unknown to the
individuals, remain fixed over time.
An organization consists of M individuals. Each individual holds a vector of beliefs about the
payoffs from the alternatives:  , with indices for individual i, time period t, and alternative
n. Individuals are endowed with initial beliefs (priors) when they join the organization. These initial priors
are developed in a pre-join learning phase of g periods. Each individual starts the pre-join phase with
uninformed beliefs in the sense that each alternative is believed to have a payoff probability of 0.5. They
then engage in individual learning about the alternatives for the duration of the pre-join phase by sampling
alternatives in a highly exploratory manner and receiving feedback on their choices. If the pre-join period
is of length g = 0, then individuals’ initial beliefs at the start of organization decision-making are identical
and uninformed. As g increases, individuals’ initial beliefs will become more accurate with respect to the
true payoff probabilities     . At low to intermediate levels of g, individuals’ priors when they
join the organization will be informative about the true payoff probabilities, but they will hold a diversity
of beliefs, with, for example, some individuals overestimating the value of low-performing alternatives and
others underestimating the value of high-performing alternatives.
In the usual bandit model setup, the extent of exploration is controlled by the temperature
parameter, τ. Higher τ leads to greater exploration. The Softmax function
  
Our model thus abstracts away from social learning, such as the sharing of knowledge and debates, which also occurs in
organizations. We discuss implications for work on social learning in the final section.
The Aggregation-Learning Tradeoff
maps an individual's beliefs of payoff probabilities for each alternative,      , which are each
on the range [0, 1], to choice probabilities 
 
     
such that
 
 (Luce 1959).
The organization aggregates individuals’ beliefs to an organizational decision based on a decision-
making structure. The M individuals’ beliefs in a given period, 
, are the basic inputs to organizational
decision making. The beliefs 
are transformed into messages. In the plurality voting decision-making
structure, each individual will transform her beliefs in a binary manner, such that the alternative with the
highest belief is 1 and the others are set to 0. In this decision-making structure, individual inclusion is
balanced, so each individual has equal weight in the organizational decision. The output of decision-making
results when the organization sums the individuals’ messages on each alternative and chooses the
alternative with the highest value. Two-stage voting is similar to plurality voting, but the input and output
of decision making occurs twice: the full set of alternatives is considered in the first round; and the top two
alternatives are considered in the second round. The average-beliefs decision-making structure functions
like plurality voting, but without any message transformation. Finally, rotating dictatorship is like plurality
voting, but with highly unbalanced individual inclusion; in each period, one randomly selected individual
is assigned a weight of 1, and all other individuals are weighted 0.
Individual learning occurs when an alternative chosen by the organization is implemented, the
outcome is observed by all individuals in the organization, and individuals update their beliefs based on
performance feedback. An individual’s belief about the payoff probability of an alternative, at a given point
in time, is the average, including the initial prior, of the observed rewards for a given alternative (March
1996). When individual learning is absent, individuals do not update their beliefs, thus their initial priors
are stable across time.
We focus on two key metrics of performance. Organizational performance in a given period is the
payoff to the alternative the organization selects in that period. Individual learning is based on the change
in individual efficacy over time. Individual efficacy in any given period is the performance an individual
would realize if, based purely on her own beliefs at that time, she were to choose the alternative she believed
to be best. Individual learning over a time window is the difference in an individual's efficacy from the start
to the end of the window.
The Aggregation-Learning Tradeoff
3.3. Model parameter settings
In our analysis, we consider organizations of M = 1, 3, 5, 7, and 9 individuals. The task environment consists
of five alternatives that vary in their performance. Specifically, these five alternatives have expected payoffs
with probabilities 0.3, 0.4, 0.5, 0.6, and 0.7. The pre-join period, in which initial prior beliefs are formed,
is five periods. Exploration is set to a level of τ = 0.01. In later analysis, we examine robustness to a wide
range of alternative parameter settings. Each simulation runs for 200 periods, and we run each simulation
10,000 times.
In this section we exercise the model. We demonstrate the tradeoff between the efficacy of information
aggregation and the extent of individual learning. In particular, we start by examining the implications of
individual learning for the efficacy of different decision-making structures, and how these decision-making
structures impact the extent to which individuals learn. We then turn to an analysis of the mechanisms
underlying these results.
4.1. Organizational performance and individual learning across decision-making structures
4.1.1. Organizational performance across decision-making structures
We first examine how organizational performance varies across decision-making structures of different
types and organizations of different sizes. In doing so, we show that decision-making structures that are
relatively effective in the absence of learning are relatively ineffective in the presence of individual learning.
The results are reported in Figure 2, Panels A and B. Panel A reports results as an average across
organizations of different organizational sizes, Panel B decomposes results by size. Organizational
performance is defined as the payoff to the choice made by the organization. The results show two cases: a
first in which there is no individual learning; and a second in which individuals learn-by-participating.
both cases, we compare organizational outcomes to the unitary actor baseline (all in period 200). The error
By 200 periods, individual learning is approaching steady state. For example, for all decision-making structures, at least 98.9 %
of the performance in period 1000 has already been reached by period 200.
In the “no individual learning” case, individuals make choices based on their beliefs, but individuals’ beliefs are not updated
based on the outcome of the organizational choice (labeled “no learning”). In the learning-by-participating case, individuals do
learn experientially by updating their beliefs based on the outcome of the organizational choice (labeled “with learning”).
The Aggregation-Learning Tradeoff
bars around the points are 95% confidence intervals (although these intervals are quite small, so hard to
[Insert Figure 2 here]
Turning first to the case of no-learning in Panel A, we see that plurality, two-stage voting, and
average-beliefs each result in higher organizational performance than does the unitary actor. This reflects
the effect of information aggregation. There is no organizational performance benefit, however, to the
rotating dictatorship structure in the no-learning case because, in a no-learning case, this decision-making
structure is functionally equivalent to the unitary actor.
There are notable differences among the plurality, two-stage voting, and average-beliefs decision-
making structures in the no-learning case in Panel A. Organizational performance varies as a function of
the degree of message transformation. Specifically, plurality voting, which embodies the greatest
information loss due to its binary message transformation, produces the lowest organizational performance
among the three decision-making structures. By contrast, the average-beliefs decision-making structure,
which employs no transformation and thus engenders no information loss, has the highest organizational
In considering the learning case, we observe a key findingdecision-making structures that are
relatively effective in the absence of learning are relatively ineffective in the presence of learning. That is,
there is a reversal in the rank ordering of decision-making structures in terms of organizational performance.
Our results thus illustrate that taking into account learning (by participating) requires a re-assessment of the
effectiveness of organizational decision-making structures.
Among the decision-making structures, rotating dictatorship stands out, as it exhibits the highest
performance among all decision-making structures we examine when individuals learn, even though it
exhibits the lowest performance in the absence of individual learning. Among the three decision-making
structures that vary along the message transformation dimension (plurality voting, two-stage voting, and
average-beliefs), plurality voting exhibits the highest organizational performance. This result is somewhat
counterintuitive as the messages communicated by individuals in the plurality voting case are those that are
most highly transformed such that they are the lossiest (least information communicated).
The Aggregation-Learning Tradeoff
In Panel B we disaggregate the results by the organizational size. Consider the no-learning case
first. Greater organizational size increases organizational performance in the case of plurality, two-stage
voting, and average-beliefs. This result underscores the well-known idea that in a setting where individual
beliefs remain static, there is value to the organization in aggregating information across a higher number
of individuals i.e., the wisdom of organizational crowds.
By contrast, when individuals learn-by-participating, the effect of organizational size disappears
for plurality voting, two-stage voting, and average-beliefs. Organizational performance achieved by each
of these decision-making structures is equivalent, regardless of organizational size. Only in the case of
rotating dictatorship is there an increase in organizational performance from increased organizational size.
4.1.2. Individual learning across decision-making structures
We next examine how individual learning varies across different decision-making structures. Individual
learning is defined as the change in individual performance over 200 periods, where individual performance
is that which individuals could achieve if they were to act alone, selecting the alternatives they believed to
be best. Results are in Figure 2 Panel C and D.
The most central resultthat constitutes our headline findingis that decision-making structures
that lead to the best organizational performance in the absence of learning (because they effectively
aggregate information) are the least effective at facilitating individual learning. Rotating dictatorship
engenders the most individual learning, followed by plurality voting, two-stage voting, and then average-
These differences in individual learning across decision-making structures explain the reversal of
organizational performance rank order once learning is taken into consideration (Panel A). Long-term
organizational performance depends in part on how effective decision-making structures are in fostering
individual learning. Simply put, organizations that foster individual learning perform better in the long-
term because they aggregate knowledge from smarter individuals who have learned more.
A comparison of learning-by-doing by the unitary actors to learning-by-participating by
organizational members reveals that in most cases the unitary actor learns more. This can be seen by
comparing plurality voting, two-stage voting, and average-beliefs to the unitary actor in Panel C. We return
The Aggregation-Learning Tradeoff
to this question below and examine whether individuals in organizations simply learn more, or whether
there is also a difference in the type of knowledge they acquire.
We note further that organizational size affects individual learning in counterintuitive ways. Larger
organizational size reduces individual learning in the case of plurality voting, two-stage voting, and
average-beliefs. While size is traditionally associated with more wisdom, individuals in larger organizations
actually learn less. The only decision-making structure for which size is positively associated with learning
is rotating dictatorship. Not only do individuals generally learn the most in rotating dictatorship, their
learning increases with the size of the organization.
In sum, our core insight is that the efficacy of information aggregation and the extent of individual
learning are inversely related, a relationship we refer to as the aggregation-learning tradeoff. In the
subsequent sections, we focus on settings in which individuals learn-by-participating to unpack the
mechanisms underlying this result.
4.2. Alignment of individual and organizational choices in learning-by-participating
The aggregation-learning trade-off is anchored in the concept of learning-by-participating. Individuals
preferred alternatives are voiced to the organization, which then selects the organizational choice via its
decision-making structure. A key consequence is that the alternative an individual selects may deviate from
the organizational choice.
We illustrate the frequency and type of deviations in the context of learning-by-participating by a
plurality voting decision-making structure using the heatmap in Figure 3.
The figure illustrates the
frequency of the different types of deviations. The cells above the diagonal illustrate cases where
organizations choose a higher-performing alternative than a randomly sampled member would choose
alone. We refer to individuals whose opinion deviates from the majority and favor a worse alternative as
incorrect contrarians. The cells below the diagonal illustrate cases where organizations choose a lower-
performing alternative than a randomly sampled member. Corresponding to our definition above, we refer
to these individuals as correct contrarians. The heatmap illustrates that incorrect contrarians (below-
The pattern is qualitatively similar in later periods, although the cell densities vary over time.
The Aggregation-Learning Tradeoff
diagonal) are more common than correct contrarians (above-diagonal). This result, while demonstrated in
the case of plurality voting, is generalizable across all decision-making structures.
[Insert Figure 3]
4.3. Individual beliefs and organizational performance
We now turn to what individuals learn, and how it affects individuals’ selections and organizations’ choices.
Our prior analysis shows that decision-making structures differ in terms of how much individuals learn and
that contrarians are common in the types of organizational decision-making we model with incorrect
contrarians outnumbering correct contrarians. Yet decision-making structures impact not only how much
individuals learn but which individuals learn and what they learn.
4.3.1. Individual beliefs on low- and high performing alternatives
The hallmark of decision-making and learning is individuals' tendency to become subject to false positives
and false negatives where they over- or under-estimate the value of some alternatives. Learning-by-doing
by a unitary actor and learning-by-participating by an organization differ markedly in this respect. In Figure
4 we show the distribution of beliefs under learning-by-participating for the worst alternative and the best,
Alternatives 1 and 5 respectively, across the various decision-making structures in the final period (for
organizations of size five).
[Insert Figure 4]
Beliefs on the lowest-performing alternative 1 display enormous variation across decision-making
structures in individuals’ tendency to overestimate this alternative. Unitary actors and individuals in rotating
dictatorship are least likely to substantially overestimate the low-performing alternative (i.e., less likely to
hold strongly false positive beliefs on bad alternatives). The reason is simple; if a unitary actor or an
individual in a rotating dictatorship overestimates the low-performing alternative substantially, she selects
the alternative, receives performance feedback, and corrects her overestimation. By contrast, in the other
decision-making structures, an individual who overestimates the lowest-performing alternative is likely to
be an incorrect contrarian. Thus, she does not learn about that alternative (because the organization is
unlikely to choose it) and her overestimation is preserved.
The Aggregation-Learning Tradeoff
A different picture emerges with respect to underestimating the value of highest-performing
alternative 5. Unitary actors frequently underestimate it, often holding strong false negative beliefs. The
underlying reason had been illustrated by Denrell and March (2001) in work on the hot stove effect:
individuals choose a high-performing alternative, receive incidental negative feedback that does not
represent the alternative’s true value, which leads to an underestimation that is then preserved, as they do
not return to the underestimated alternative. Our analysis shows that learning-by-participating offers a
remedy against the hot stove effect. Even if an individual accidentally underestimates the alternative, and
thus does not favor it, she is likely to receive more performance feedback on the alternative because other
members of her organization are likely to favor it (given its high performance), making it the organization’s
choice, and resulting in more feedback which then allows her to correct her underestimation.
The analysis illustrates how learning-by-doing by a unitary actor and learning-by-participating by
an organization differ, in particular with respect to what individuals learn. Individual learning-by-doing
provides a relatively nuanced and accurate understanding of low-performing alternatives in the sense that
poor alternatives are quickly ruled out. Organizational learning-by-participating provides a relatively
nuanced and accurate understanding of high-performing alternatives in the sense that, for instance, the
organization can more effectively distinguish between the top two alternatives.
4.3.2. Disconnect between individual beliefs and organizational choices
The learning-by-participating process can engender a rather stark disconnect between the extent of false
beliefs (false positives/negatives on poor/good alternatives) and organizational choices as the outcome of
the decision-making structure. Figure 5 contrasts the alternatives individuals believe to be best (Panel A)
and the alternatives organizations choose (Panel B).
[Insert Figure 5 here]
Panel A shows that unitary actors and members of rotating dictatorships are almost never subject
to false positives, while members of the other decision-making structures are, which echoes our findings in
Figure 4. The process of information aggregation leads to the persistence of these false positive beliefs in
the plurality voting, two-stage voting and average-beliefs decision-making structures. Among all three of
these structures, as organizational size increases, the fraction of false positive individuals also increases, a
contrast with what happens in rotating dictatorship decision-making.
The Aggregation-Learning Tradeoff
Consider how individuals’ beliefs translate to organizational choices. In Panel B we show the
distribution of alternatives selected by the organization when individuals learn-by-participating in decision
making (in period 200). Comparing Panel B with Panel A produces a somewhat surprising observation. In
decision-making structures where many individuals remain subject to false positive beliefs, organizations
rarely choose the worst alternative. They marginalize contrarians and are thus able to choose high-
performing alternatives. The contrast between Panel A and Panel B illustrates an important facet of the
aggregation-learning trade-off: decision-making structures like plurality, two-stage voting and average
beliefs are effective in aggregation as they marginalize contrarians, and thus forgo picking low-performing
alternatives, but it is that very marginalization that also keeps individuals from learning, as they cannot
influence the organization to implement their preferred alternative and thus, cannot correct their false
Panel B also reveals the answer to why organizational decision-making structures that are effective
in aggregation nevertheless perform relatively poorly when learning is allowed (as observed in Figure 2,
Panel C and D). While such structures are very effective in avoiding the lowest-performing alternative, they
are challenged in distinguishing amongst the high performing alternatives.
4.3.3. Leveraging contrarians to enhance organizational performance
There is merit in leveraging the wisdom of contrarians, evidently when they are correct, but even
when they are incorrect; and decision-making structures vary in their ability to do so. Often organizations
fail to leverage individuals who are contrarians in period t in their decisions in period t +1, and thus
contrarians in one period may remain marginalized as contrarians in subsequent periods. We plot the
fraction of individuals who are correct versus incorrect contrarians in their organizations in period 200 in
Figure 6. A number of results stand out. First rotating dictatorship produces far fewer contrarians than do
the other decision-making structures. Second, the probability of an individual being a contrarian is
increasing in size. Third, contrarians are much more likely to be incorrect than correct by period 200.
Finally, among plurality voting, two-stage voting, and average-beliefs, increasing the extent of message
transformation (i.e., to binary in plurality voting) leads to a decrease in the number of contrarians in the
long run.
[Insert Figure 6 here]
The Aggregation-Learning Tradeoff
The capacity to leverage the wisdom of contrarians is an important feature of decision-making
structures when individuals learn-by-participating, and decision-making structures vary in their ability to
do so. It is evident that the presence of contrarians is positively correlated with the long-term performance
of a decision-making structure. Contrarians play an important role in the efficacy of different decision-
making structures when individuals learn. Clearly, if a contrarian is correct in recognizing the best
alternative, marginalizing her by excluding her knowledge is problematic. Less obvious is that there are
unexpected long-term costs to marginalizing some incorrect contrarians. This is because incorrect
contrarians may still hold valuable knowledge regarding the merits of the other alternatives.
To illustrate the efficacy of decision-making structures in leveraging contrarians, consider two
types of individuals. One type is what we call a genius. This is an individual who, in period 1, correctly
believes that alternative 5 is the highest payoff. By contrast, another type of individual is what we call an
anti-genius. This is an individual who, in period 1, incorrectly believes that alternative 1 is the highest
payoff. Either a genius or an anti-genius can be a contrarian if they are, within their organization, small in
number. For instance, one anti-genius in an organization of size five is likely to be an incorrect contrarian,
but if there were three anti-geniuses, they would no longer be contrarians.
We start by examining the implications of geniuses. We illustrate that performance differences
across decision-making structures stem in part from the ability to leverage small numbers of geniuses. In
Figure 7, across decision-making structures and size, we show organizational performance after 200 periods
of learning-by-participating as a function of the number of individuals who are geniuses. The y-axis shows
organizational performance, and the lines plot the average performance of organizations with a specific
number of geniuses (0, 1, 2, 3).
[Insert Figure 7 here]
It is somewhat self-evident that, regardless of decision-making structure, performance should
increase with the number of geniuses. Decision-making structures that are balanced with respect to
individual inclusion in decision-making (plurality voting, two-stage voting, average-beliefs) function
relatively poorly when geniuses are few in number, as these are likely to get marginalized. For instance,
one genius in a plurality voting decision-making structure is likely to be a contrarian, and will have little
influence on the organization’s choice—in this sense, the plurality decision-making structure bears the cost
The Aggregation-Learning Tradeoff
of not being able to make effective use of this knowledge. When a genius does not influence the
organizational decision, others in the organization are not exposed to the genius’ knowledge regarding the
highest-payoff alternative. By contrast, in the rotating dictatorship decision-making structure, a single
genius is sufficient for the organization to reach a high level of performance. In this structure, a genius,
when she gets her turn to make the organizational decision, chooses the highest-performing alternative, and
the other members of the organization can then learn about the true value of that alternative
Organizations of different sizes are differentially sensitive to the number of geniuses they contain.
For instance, one genius in a plurality decision-making structure of size three will have a substantially
greater positive impact on organizational performance than will that individual in a larger organization.
There is a sharp downward slope in performance as organizational size increases among the decision-
making structures that involve balanced individual inclusion. By contrast, rotating dictatorship is somewhat
less sensitive to organizational size because even a single genius will eventually get her turn to make the
organizational decision in a large organization. This also explains why rotating dictatorship is the only
decision-making structure for which an increase in size has a clearly positive effect in the learning-by-
participating case (Figure 2, Panel B). The larger the organization, the higher the chance that it harbors at
least one genius from whom the organization can learn.
Taken together, the extent of message transformation across these decision-making structures has
a substantial impact on whether a genius, when she is a contrarian (she would be a correct contrarian), is
able to influence the organizational choice. The mechanism described above, by which organizations vary
in their capacity to tap into the useful knowledge of geniuses, is a close cousin of the mechanism by which
organizations leverage the knowledge of correct contrarians, and explains in part the difference in the
distribution of beliefs regarding the high-performing alternative 5 (per Figure 4 discussed earlier).
We now turn to anti-geniuses individuals who believe, in period one, that the worst alternative
is best. Such individuals are highly likely to be contrarians in the early periods. Differences across decision-
making structures in the capacity to make effective use of individual knowledge stem from the ability to
The difference across plurality, two-stage voting, and average beliefs is due to the difference in message
transformation. The stronger the message transformation, the smaller the alignment (or correlation) among
organizational members’ messages. A reduction of alignment increases the chance of a contrarian to influence the
organization’s decision.
The Aggregation-Learning Tradeoff
leverage the knowledge of anti-geniuses. It might seem, on the surface, that this is an absurd idea. But anti-
geniuses may not be devoid of useful knowledge. While they may well believe, incorrectly, that alternative
1, the lowest-payoff alternative, is the highest-payoff alternative, they may also believe that alternative 5 is
better than alternative 4, and indeed, that alternatives 2 and 3 are mediocre. In our model, for the set of anti-
geniuses, 54.3 percent correctly believe at the outset that alternative 5 is superior to alternative 4, even
though they incorrectly believe that alternative 1 is best. In this sense, even anti-geniuses may hold
knowledge that is valuable to the organization.
This insight helps explain why rotating dictatorship performs so wellit can make more effective
use of the knowledge of anti-geniuses, who tend to be incorrect contrarians, than do the other decision-
making structures. In plurality voting, if the anti-genius votes time and time again for the worst alternative,
alternative 1, she will not contribute to the organization's efforts to distinguish alternative 4 from the
superior alternative 5. Yet the rotating dictatorship decision-making structure will eventually give the anti-
genius her turn at making the organizational decision. She will then choose alternative 1, which she
erroneously favors, and will be likely to observe negative feedback that will then help her recognize that it
is a low-payoff alternative, and to thus correct her false positive belief. The next time she gets her way in
the organization, she will be more likely to select alternative 5, the best alternative, and in doing so, help
the organization distinguish between the best and second best alternatives.
Interestingly, it can be the case that it is not strictly better to have fewer anti-geniuses. One would
expect that in choice problems the addition of an incremental anti-genius is detrimental to performance.
However, an additional anti-genius in the organization can actually increase long-run performance in the
plurality voting, two-stage voting, and average-beliefs decision-making structures. In these decision-
making structures, a lone anti-genius is an incorrect contrarian and is unlikely to receive feedback on the
alternative she thinks is (erroneously) best. By contrast, if there are two or three such anti-geniuses, she
may not be a contrarian. This in turn allows feedback on that poor alternative, which subsequently unlocks
their knowledge on the other alternatives.
4.4. Robustness analysis and model extensions
The results presented above highlight mechanisms by which the performance of decision-making structures
vary when individuals learn-by-participating in decision-making. We conducted additional analyses to
The Aggregation-Learning Tradeoff
establish the robustness of our insights and illustrate some contingencies. All references to figures below
refer to those in the Appendix.
First, we examine short run learning-by-participating results. In the main models, we considered
the no-learning case, and the case of individual learning over 200 periods. In Figure A1, we add a learning
result at the 25th period. The results are interesting for rotating dictatorship. After 200 periods of learning,
this decision-making structure is clearly the best performer, both organizationally and in terms of individual
learning. Yet we see that at period 25 this decision-making structure underperforms the plurality voting,
two-stage voting, and average-beliefs decision-making structures in terms of organizational performance.
Rotating dictatorship is quite exploratory, which facilitates individual learning, and which comes at a
substantial short-run cost to organizational performance.
Second, we examine how individuals’ initial knowledge diversity impacts the performance of
different decision-making structures. In our model, individuals are endowed with initial beliefs (priors)
when they join the organization. These initial priors are developed in a pre-join learning phase of g periods
in which individuals independently sample alternatives in a highly exploratory manner and receive feedback
on their choices. After this pre-join phase, individuals join the organization. In the main models, g = 5.
Reducing the length of this pre-join phase reduces initial diversity in beliefs, while increasing it does the
opposite. In Figure A2, we set the pre-join period to g = 1, and observe that the pattern of results is quite
similar to our main results in Figure 2. In Figure A3, we set the pre-join period to g = 50. In this situation,
there is substantially less initial knowledge diversity as individuals have nearly reached steady state
knowledge, converging on good alternatives even before they join the organization. The results differ
slightly from our main findings. It is obvious that individual learning is much reduced in this setting. More
interesting is that we observe a positive impact of organizational size on performance when individuals
learn for the plurality voting, two-stage voting, and average-beliefs structures, which is not observed in our
main results. Size has a positive effect because individuals hold fewer false positives when they join the
organization with the extended pre-join phase. Thus, the downside of size, that individuals are less able to
correct false positives, is greatly reduced.
Third, we examine the implications of increasing the organizational size. In our main analysis we
consider sizes three through nine, as these reflect a reasonable range for decision-making groups. Of course,
The Aggregation-Learning Tradeoff
one can imagine larger decision-making groups. To assess the implications of larger size, we examine
settings with 31, 51, and 101 individuals. The results are in Figure A4. As one would expect, in the no-
learning case, increasing organizational size has a large positive impact on organizational performance.
This is no surprise as it is purely the case of information aggregation (as in the wisdom of crowds). Indeed,
as the size gets arbitrarily large, organizational performance approaches 0.7 (maximum possible in this
setting). In the learning case, increasing size offers no additional performance benefits over smaller sizes,
and long run performance is below that of the no-learning case. This is because if the initial information
aggregation leads to the choice of the best alternative in the absence of learning, then learning can offer no
benefits, and learning’s myopias come to dominate. In particular, because even the best alternative (with a
payoff of 0.7 in our model) produces negative results 30 percent of the time, hot stove effects (Denrell and
March 2001) may lead the learning organization to abandon the good alternative and, quite possibly, never
return to it. This is exacerbated because larger organizations employing the plurality, two-stage and average
beliefs decision-making structures are very unlikely to explore, even if the individuals are quite exploratory.
The exception is rotating dictatorship, in which the no-learning case (which lacks information aggregation)
is far inferior to the learning case, as the latter is able to resolve hot-stove issues by randomly allocating
Fourth, we examine the implications of varying the task environment characteristics. In our main
models, we consider five alternatives with payoffs of        In Figures
A5-A8, we examine environments that have: (A5) more choice alternatives, (A6) choice alternatives that
are more munificent, (A7) choice alternatives that are less munificent, and (A8) choice alternatives that are
more similar. While the results vary quantitatively, they are qualitatively robust in that the rank ordering of
decision-making structures with respect to performance and learning remains unchanged.
Fifth, we consider two forms of variation along the exploitation-exploration continuum. In our main
models, we set τ = 0.01, which is a very mild level of exploration. We tested the implications of making
individuals greedy, with τ approaching zero. The results, which are in Figure A9, are largely unchanged. It
is worth noting that even in the absence of individual exploration, organizations can explore because of
individuals’ heterogeneous beliefs. In addition, we relax that assumption that, in the context of plurality
voting, votes are counted accurately. We configured the model with a probability of 0, 0.1, or 0.2 that any
The Aggregation-Learning Tradeoff
participant’s vote is counted twice or not at all. We find that an increase in the level of noise decreases the
benefits of organizational aggregation, given that the weighting of votes is random and results in the
marginalization (or even exclusion) of valuable knowledge.
Finally, we considered alternative characterizations of organizational contrarians and geniuses. In
our main analysis, we defined a contrarian as an individual who favors a choice alternative (i.e., the
alternative they believe is best) that differs from that chosen by the organization. We examine a different
formulation based on the Spearman correlation between individuals’ beliefs in an organization. The results
are in Figure A10, which is quite similar to Figure 6 in the paper. Likewise, we can use Spearman
correlations to define geniuses. The results are in Figure A11, which is quite similar to Figure 7 in the paper.
Taken together, these results suggest that different measures of contrarians and geniuses do not change our
interpretation of the mechanism by which they act.
We examine the dual role of structure in aggregating individuals’ beliefs and shaping individuals’ learning.
Our central argument is that when individual learning within organizations is possible (“learning-by-
participating”), alternative decision-making structures give rise to an aggregation-learning tradeoff wherein
decision-making structures that more effectively aggregate individuals’ knowledge can be less effective at
facilitating individuals’ learning. An implication of this insight is for work comparing the effectiveness of
organizational structures. This work often does not consider the role of learning. We show that when
learning is taken into account, the rank order of structures’ effectiveness may change markedly. Beyond
this insight, our paper has theoretical implications for a host of issues in the domain of organizations,
including learning, microfoundations, teams, and crowds.
5.1. Implications for theory
5.1.1. Learning processes in organizations
This paper has implications for the vast body of work on social learning. A central focus of this work has
been on the network ties that connect individuals, with network structure seen as a key factor shaping how
and what individuals learn (e.g., Granovetter 1973, March 1991, Hansen 1999, Burt 2004, Fang, Lee and
The Aggregation-Learning Tradeoff
Schilling 2010; Aral and Van Alstyne 2011, Tortoriello, Reagans, and McEvily 2011, Padgett and Powell
2012, Clough and Piezunka 2020). We show that there is an important channel for learning that operates
orthogonally to informal network structurenamely, the formal decision-making structure employed by
the organization. Because decision-making structures shape the trajectory of individual knowledge over
time, they can operate in tandem with the effects of social networks. These structures are of course
particularly salient to decision-making in organizational settings, where repeated interactions among
individuals engender individual-level learning alongside the effects of social influence (e.g., Le Mens and
Denrell 2011, Denrell and Le Mens 2007).
In addition to demonstrating that alternate decision-making structures can lead to variation in
individual-level learning outcomes, our research also points to challenges for individual learning that stem
from individuals being surrounded by knowledgeable others in an organization (Ingram and Simons 2002,
Waldinger 2012, KC, Staats and Gino 2013, Hwang, Singh, Argote 2015, Myers 2018). Prior research
points to the difficulties inherent in social learning from well-performing peers (Posen, Yi and Lee 2020).
Our work, on the other hand, suggests that there may be equally important challenges to learning with well-
performing peers. The upside of being in an organization with knowledgeable peers of course is that
individuals can learn about high-performing alternatives. The downside, however, is that individuals may
not have the opportunity to learn about low-performing alternatives. As a consequence, if and when an
individual acts on her own, she may have little knowledge of the implications of low-performing
alternatives, making her more likely to select alternatives from this low-performing set.
Our work also demonstrates the consequences of enabling or avoiding mistakes” in organizational
decisions. Although individuals learn from mistakes, organizations often strive to keep employees from
making mistakes. We find that decision-making structures that make the fewest mistakes are also the ones
that are least effective in fostering individuals’ learning, as well as the ones that perform the worst over the
long-term. This insight, though based on a different mechanism, relates to work by Stan and Vermeulen
(2013), who show that hospitals that want to avoid failure (and thus reject certain challenging patients) are
The Aggregation-Learning Tradeoff
the ones that learn the least. Our study illustrates the need for organizations to create opportunities for
employees to fail, and moreover to learn from that failure.
5.1.2. Microfoundations and individual agency
We also advance our understanding of the microfoundational approach to organizations. The central
question in the domain of organizational microfoundations is how individual-level factors interact and
aggregate to shape macro, organizational-level phenomena (Barney and Felin 2013, Felin, Foss and
Ployhart 2015, Aggarwal et al. 2017, Schilke 2018, Davis and Aggarwal 2020, Piezunka and Schilke 2021).
We show that there is an ongoing recursive interaction between the organization and the individual (e.g.,
Coleman 1990), wherein the structures used to aggregate individual decisions in an organization shape what
and how individuals learn, and as a consequence inform the subsequent actions of individuals over time.
This demonstrates not only that the mapping from individual beliefs to individual and organizational actions
is difficult to cleanly disentangle from the structure that governs organizational decision-making, but more
importantly, it enables us to gain insight into the learning-by-participating process, which operates as a
fundamental mechanism of aggregation linking together the individual and organizational levels of analysis.
The cross-level interactions engendered by this aggregation process, furthermore, lead to
interesting tradeoffs across organizational levels. While individuals benefit (themselves) from learning,
organizations benefit (at least in the short-term) from effective aggregation. This tradeoff points to the
possibility of agency conflict in which individuals may favor decision-making structures that differ from
those that are in the best interests of the organization. Although over the long-term, maximizing individual
learning may also maximize organizational performance, this form of agency conflict can persist.
Agency conflict within organizations arising from aggregation processes (and their inherent
tradeoffs) further relates to individual-level career considerations. Research on individual careers has
examined how factors such as peers (Waldinger 2012), employers (Sutton and Callahan 1987, Burton,
Sørensen and Beckman 2002, Bidwell, Won, Barbulescu and Mollick 2015), entrepreneurship (Sorenson,
Dahl, Canales and Burton 2021), and education (Lazear 2004, Eesley, Li and Yang 2016, Eesley and Lee
2020) affect various features of the individuals’ careers such as income and employability. We add another
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important feature to this debate, the structure of the learning process, and show that it can make a significant
difference whether an actor learns by doing or by participating; and for the latter, in which kind of decision-
making structure.
5.1.3. Teams and structure
Our work speaks to the importance for groups, committees, top management teams, and boards to consider
the decision-making structure under which they operate. A large literature on top management teams
examines the characteristics, education, and experience of top managers for firms’ decisions and outcomes
(e.g., Hambrick and Mason 1984, Haleblian and Finkelstein 1993, Chen, Crossland and Huang 2016).
Likewise, there is a growing literature on entrepreneurial teams (e.g., Tarakci, Greer, and Groenen 2016,
Garg, Li and Shaw 2018, Hoppmann, Naegele and Girod 2019, Ganco, Honoré, and Raffiee 2019, Clough
et al. 2019). Much of this work points to factors that help explain success, such as hierarchy (Lee 2021, Lee
and Csaszar 2021), faultines (Vissa 2010), and knowledge distribution (Aggarwal, Hsu and Wu 2020). With
the recent exception of Chen, Elfenbein, Posen and Wang (2021), however, the question of the effective
design of decision-making in entrepreneurial teams has received little attention. Although team design is
clearly a multifaceted topic, our research suggests there may be merits to a renewed focus on information
processing and decision-making in the Simon (1947) tradition, which may be extended to not only consider
the role of information aggregation across team members, but also the extent of individual learning, and
how these two functions interact.
In considering the issue of team design, one of our most interesting results is the effectiveness of
the rotating dictatorship decision-making structure. By allowing individuals to revisit, and thereby correct
their beliefs, rotating dictatorship may provide a remedy against the hot stove effect (Denrell and March
2001), and as such it may be useful tool organization designers considering the ways in which teams should
be structured. Yet, if this structure is so effective, why is it not more commonly used in organizations? One
answer is that it is ineffective in the absence of learning, or when only short periods of learning are available.
Rotating dictatorship, for example, involves making early mistakes that then allow for the correction of
individuals erroneous (false positive) beliefs about the merits of the alternatives. To the degree that the
The Aggregation-Learning Tradeoff
organization operates in an environment that is less forgiving of early mistakes, the relative value of rotating
dictatorship as a team decision-making structure may be diminished.
5.1.4. Crowd-based decisions
We contribute to research on organizational design that intersects with work on the wisdom of crowds
(Becker, Porter and Centola 2019, Le Mens, Kovács, Avrahami and Kareev 2018). The ‘crowds’ in
organizations differ from traditional crowds in a manner that has important implications for organizational
design. First, crowds in organizations include a relatively small number of individuals. Second, individuals
in organizations often engage in repeated decision-making through which they have the opportunity to learn
from feedback on prior decisions (Christensen and Knudsen 2009). Scholars have increasingly sought to
apply the underlying ideas of the wisdom-of-crowds to organizational decisions (Page 2007), implying that
the wisdom-of-crowds idea (Surowiecki 2005, Atanasov et al. 2016) is directly applicable. Our study
suggests, however, that a naive application of the wisdom-of-crowds logic may lead to decision-making
structures with inferior long-run performance outcomes.
Our study also informs the linkage between the size of crowds and decision effectiveness. While
prior work suggests larger crowds to be more effective, we show that greater size creates challenges for
individual-level learning because it restricts opportunities for individuals to influence the organizational
decision. The size of crowds in organizations is thus a double-edged sword: if a key objective is to develop
smarter individuals, focusing on greater size and information aggregation may be counterproductive.
Our work also intersects with the related, but very different, body of work on crowdsourcing. While
both tap into the potential of the crowd, models of information aggregation design structures that utilize
redundancy, whereas crowdsourcing thrives on diversity (Girotra, Terwiesch and Ulrich 2010; Jeppesen
and Lakhani 2010; Boudreau 2012, Afuah and Tucci 2012; Felin, Lakhani and Tushman 2017). Our
research illustrates how these streams can inform one another. Behavioral research on crowdsourcing
illustrates that organizations succeed in eliciting a pool of diverse ideas, but typically end up selecting
redundant ideas (Piezunka and Dahlander, 2015). Despite the appeal of the idea that organizations may
thrive on diversity, organizations seem to value redundancy when they try to leverage the wisdom of the
The Aggregation-Learning Tradeoff
crowdsand fail to benefit from crowdsourcing as a consequence (Dahlander and Piezunka, 2020). Our
results illustrate that commonly used decision-making structures such as plurality voting, which on the
surface seem to encode redundancy, tend to marginalize contrarians, and in doing so, undermine
individuals’ learning.
5.2. Managerial implications
A number of managerial implications flow from our theoretical insights. Most notably, our paper has
implications for how managers should design their organizationi.e., which decision-making structure
they should pick. In settings where individual learning over time is feasible, our work suggests that long-
term performance is optimized by choosing an organization structure that engages in greater message
transformation. Managers should also carefully examine the number of people involved in decision-making,
given that increasing size is costly and its positive effect may be smaller than that associated with facilitating
learning. The outlined mechanism on the integration of contrarians should caution managers in how they
can engage with contrarians. Even if a manager is certain that a contrarian favors the wrong alternative, the
manager would still benefit from tapping into the contrarian’s knowledge on other alternatives.
An organization not only makes choices about the design of decision-making premised on its
aspired level of performance, but also how this performance is generatedvia information aggregation or
individual learning. This suggests implications for the types of organizations individuals may wish to join.
Decision-making structures vary in the degree to which they foster the learning of their members. An
individual that considers joining an organization should carefully consider what effect her set of beliefs will
have on an organization, given the set of beliefs of current members of the organization, as well as the
organization’s design. This also implies that managers should consider how they cope with contrarians.
Instead of marginalizing contrarians and allowing them to become a disgruntled minority, managers might
allow them to influence the organizational decision in order to integrate their knowledge and learning for
the benefit of the organization.
5.3. Limitations and future research
The Aggregation-Learning Tradeoff
We have made progress toward examining the dual role of structure in shaping both organizational
performance as well as individual-level learning in organizations by holding fixed mechanisms of learning
beyond learning-by-participating that may occur within organizations. Incorporating other (non-
experiential) learning mechanisms into future theory development efforts, such as social learning among
members of the organization, and other contingencies outside the scope of the present paper, may offer
fruitful opportunities for richer theoretical insights.
One avenue for future research may be to study the implications of relaxing the assumption that
individuals’ votes are equally weighted (either within a period or over time). This is related to the issue of
power dynamics in organizations. In a simple sense, this may be reflected in our model via heterogeneity
in voting rights across individuals (i.e., differences in heterogeneity among individuals with respect to their
decision-making weights). Of course, this may become much more complicated when voting rights emerge
endogenously from effective (or ineffective) outcomes from prior voting. Not only may voting rights
emerge endogenously, but so too may the groups of individuals participating in decision-making. The
formation and dissolution of groups (e.g., Carley 1991) has received relatively little attention in the
management literature (likewise with work on the closely related idea of coalitions in Cyert and March
1963). However, group formation may have important implications for the efficacy of learning-by-
participating. This relates to the question of “who is in the room” (Dobrajska, Billinger and Becker 2014).
More empirical data on this underlying process would complement insights on how decisions are made,
and the role of learning-by-participating in this regard. Future research may also endogenize individuals’
voting behavior as the decision-making structure may affect people’s willingness to vote sincerely (Sah and
Stiglitz 1986; Piezunka and Schilke 2021).
A fertile avenue for future research may also be to examine the tradeoffs that arise among different
decision-making structures when learning-by-participating occurs. Plurality voting, two-stage voting, and
average beliefs, for example, exhibit similar performance in the long-run, but differ with respect to how
much individuals in the respective organizations have learned. Future research might build on a logic of
value creation and value capture to further explore these tradeoffs. In addition, numerous contingencies
The Aggregation-Learning Tradeoff
may shape our understanding of the tradeoffs across decision-making structures. Beyond the evident short-
versus long-run tradeoff, the size of the organization also plays an important role. Future work may examine
issues such as environmental turbulence and differences in individual rates of learning. It may be the case,
for example, that individuals learn differently by observing organizational outcomes in which they
themselves are contrarians versus decisions in which they are a part of the dominant organizational
coalition. Understanding the differential trajectories of individual learning that arise in such a situation can
add further color to our understanding of the aggregation-learning tradeoff.
5.4. Conclusion
In summary, we examine the tradeoff between aggregation and learning in organizations. To do so, we
bridge work on information aggregation and organizational learning by examining the implications of
learning-by-participating for the efficacy of different structures of organizational decision-making. Using
a computational model, we find that the efficacy of information aggregation and the extent of individual
learning are inversely related outcomes whose ultimate impact on organizational performance is
adjudicated by the way in which organizational contrarians, individuals who favor choices that differ from
that of the organization, influence organizational choices. Our insights have implications for future research
on organizational decision-making across a range of organizational contexts, including groups, teams,
boards, and crowds.
The Aggregation-Learning Tradeoff
Adner R, Csaszar FA, Zemsky B (2014) Positioning on a multiattribute landscape. Management Science
60(11): 2794-2815.
Afuah A, Tucci CL (2012) Crowdsourcing as a solution to distant search. Academy of Management Review
37(3): 355-375.
Aggarwal VA, Hsu DH, Wu A (2020) Organizing knowledge production teams within firms for innovation.
Strategy Science 5(1): 1-16.
Aggarwal VA, Posen HE, Workiewicz M (2017) Adaptive capacity to technological change: A
microfoundational approach. Strategic Management Journal 38(6): 1212-1231.
Aral S, Van Alstyne M (2011) The diversity-bandwidth trade-off. American Journal of Sociology 117(1):
Argote L, McEvily B, Reagans R (2003) Managing knowledge in organizations: An integrative framework
and review of emerging themes. Management Science 49(4): 571-582.
Argote L, Miron-Spektor E (2011) Organizational learning: From experience to knowledge. Management
Science 22(5): 1121-1367.
Arrow K (1951) Social Choice and Individual Values (Wiley, New-York).
Atanasov P, Rescober P, Stone E, Swift SA, Servan-Schreiber E, Tetlock P, Ungar L, Mellers B (2016)
Distilling the wisdom of crowds: Prediction markets vs. prediction polls. Management Science 63(3):
Balinski M, Laraki R (2007) A theory of measuring, electing, and ranking. Proceedings of the National
Academy of Sciences 104(21): 8720.
Barberà S, Sonnenschein H, Zhou L (1991) Voting by committees. Econometrica 59(3): 595-609.
Barney J, Felin T (2013) What are microfoundations? Academy of Management Perspectives 27(2): 138-
Baumann O (2015) Models of complex adaptive systems in strategy and organization research. Mind and
Society 14(2): 169-183.
Becker HS (2017) Creativity is not a scarce commodity. American Behavioral Scientist 61(12): 15791588.
Becker J, Porter E, Centola D (2019) The wisdom of partisan crowds. Proceedings of the National Academy
of Sciences USA 116(22): 10717-10722.
Ben-Yashar RC, Nitzan SI. (1997) The optimal decision rule for fixed-size committees in dichotomous
choice situations: The general result. International Economic Review 38(1): 175-186.
Bernstein E, Shore J, Lazer D (2018) How intermittent breaks in interaction improve collective intelligence.
Proceedings of the National Academy of Sciences USA 115(35): 87348739.
Bidwell M, Won S, Barbulescu R, Mollick E (2015) I used to work at Goldman Sachs! How firms benefit
from organizational status in the market for human capital. Strategic Management Journal 36(8): 1164-
Bodily SE (1979) A delegation process for combining individual utility functions. Management Science
25(10): 1035-1041.
Boudreau KJ (2012) Let a thousand flowers bloom? An early look at large numbers of software App
developers and patterns of innovation. Organization Science 23(5): 1213-1522.
Burt RS (2004) Structural holes and good ideas. American Journal of Sociology 110(2): 349-399.
Burton MD, Sorensen JB, Beckman CM (2002) Coming from good stock: Career histories and new venture
formation. Lounsbury, M. And Ventresca, M.J. (Ed.) Social Structure And Organizations Revisited
(Research In The Sociology Of Organizations Emerald Group Publishing Limited, Bingley 19: 229-262.
Carley KM (1991) Designing organizational structures to cope with communication breakdowns: A
simulation model. Industrial Crisis Quarterly 5(1): 19-57.
Chatterji S, Sen A, Zeng H (2014) Random dictatorship domains. Games and Economic Behavior 86: 212-
Chen G, Crossland C, Huang S (2016) Female board representation and corporate acquisition intensity.
Strategic Management Journal 37(2): 303-313.
The Aggregation-Learning Tradeoff
Chen J, Elfenbein D, Posen H, Wang MZ (2021) The problems and promise of entrepreneurial partnerships:
Decision making, overconfidence, and learning in founding teams. Academy of Management Review
Christensen M, Knudsen T (2009) The architecture of knowledge organization In Foss NJ, Michailova S
(Eds), Knowledge Governance: Processes and Perspectives (Oxford University Press, Oxford)
Christensen M, Knudsen T (2010) Design of decision-making organizations. Management Science 56(1):
Clough DR, Vissa B (2018) How do founding teams form? Towards a behavioral theory of founding team
formation. Academy of Management Proceedings 2018 Issue 1
Clough DR, Fang TP, Vissa B, Wu A (2019) Turning lead into gold: How do entrepreneurs mobilize
resources to exploit opportunities? Academy of Management Annals 13(1): 240271.
Clough DR, Piezunka H (2020) Tie dissolution in market networks: A theory of vicarious performance
feedback. Administrative Science Quarterly 65(4): 9721017.
Coleman JS (1990) Foundations of Social Theory (Harvard University Press, Cambridge, MA).
Criscuolo P, Dahlander L, Grohsjean T, Salter A (2017) Evaluating novelty: The role of panels in the
selection of R&D projects. Academy of Management Journal 60(2): 433460.
Csaszar FA (2013) An efficient frontier in organization design: Organizational structure as a determinant
of exploration and exploitation. Organization Science 24(4): 1083-1101.
Csaszar FA, Eggers JP (2013) Organizational decision making: An information aggregation view.
Management Science 59(10): 22572277.
Cyert RM, March JG (1963) A Behavioral Theory of the Firm (Blackwell, Malden, MA).
Dahlander L, O'Mahony S (2011) Progressing to the center: Coordinating project work. Organization
Science 22(4): 961-979.
Dahlander L, Piezunka H (2020) Why crowdsourcing fails. Journal of Organization Design 9(1): 1-9.
Davis JP, Aggarwal VA (2020) Knowledge mobilization in the face of imitation: Microfoundations of
knowledge aggregation and firm-level imitation. Strategic Management Journal 41(11): 1983-2014.
Davis JP, Eisenhardt KM (2011) Rotating leadership and collaborative innovation: Recombination
processes in symbiotic relationships. Administrative Science Quarterly 56(2): 159201.
Denrell J, Le Mens G (2007) Interdependent sampling and social influence. Psychological Review 114(2):
Denrell J, March JG (2001) Adaptation as information restriction: The hot stove effect. Organization
Science 12(5): 523-538.
Dobrajska M, Billinger S, Becker M (2014) How firms make boundary decisions. Academy of International
Business. Annual Meeting Proceedings.
Eesley C, Lee YS (2020) Do university entrepreneurship programs promote entrepreneurship? Strategic
Management Journal 1 29.
Eesley C, Li JB, Yang D (2016) Does institutional change in universities influence high-tech
entrepreneurship? Evidence from China’s project 985. Organization Science 27(2): 446-461.
Fang C, Lee J, Schilling MA (2010) Balancing exploration and exploitation through structural design: The
isolation of subgroups and organizational learning. Organization Science 21(3): 625-642.
Felin T, Foss NJ, Ployhart RE (2015) The microfoundations movement in strategy and organization theory.
Academy Of Management Annals 9(1): 575-632.
Felin T, Lakhani KR, Tushman ML (2017) Firms, crowds, and innovation. Strategic Organization 15(2):
Fishburn PC (1974) Simple voting systems and majority rule. Behavioral Science 19(3): 166-176.
Fishburn PC, Gehrlein WV (1976) An analysis of simple two-stage voting systems. Behavioral Science.
Galton F (1907) Vox populi (the wisdom of crowds). Nature 75(7):450-451.
Ganco M, Honoré F, Raffiee J (2019) Entrepreneurial team assembly: knowledge transfer, customer
transfer, and matching models In Reuer FJ, Matusik SF, Jones J (Eds), The Oxford Handbook of
Entrepreneurship and Collaboration (Oxford University Press, Oxford)
The Aggregation-Learning Tradeoff
Garg S, Li Q, Shaw J (2018) Undervaluation of directors: Impact on turnover of directors (and CEO) in
newly public firms. Strategic Management Journal 39(2): 429-457.
Gibbard A (1977) Manipulation of schemes that mix voting with chance. Econometrica 45(3): 665681.
Girotra K, Terwiesch C, Ulrich KT (2010) Idea generation and the quality of the best idea. Management
Science 56(4): 591-605.
Granovetter MS (1973) The strength of weak ties. American Journal of Sociology 78(6): 1360-1380.
Greve HR (2003) Organizational Learning from Performance Feedback: A Behavioral Perspective on
Innovation & Change (Cambridge University Press, Cambridge, UK).
Haleblian J, Finkelstein S (1993) Top Management Team Size, CEO Dominance, and firm Performance:
The Moderating Roles of Environmental Turbulence and Discretion. Academy of Management Journal
36(4): 844-863.
Hambrick DC, Mason PA (1984) Upper echelons: The organization as a reflection of its top managers.
Academy of Management Review 9(2): 193-206.
Hansen MT (1999) The search-transfer problem: The role of weak ties in sharing knowledge across
organization subunits. Administrative Science Quarterly 44(1): 82-111.
Hastie R, Kameda T (2005) The robust beauty of majority rules in group decisions. Psychological Review
112(2): 494-508.
Hoppmann J, Naegele F, Girod B (2019) Boards as a source of inertia: Examining the internal challenges
and dynamics of boards of directors in times of environmental discontinuities. Academy Of
Management Journal 62(2): 437-468.
Hwang EH, Singh PV, Argote L (2015) Knowledge sharing in online communities: Learning to cross
geographic and hierarchical boundaries. Organization Science 26(6): 1593-1611.
Ingram P, Simons T (2002) The Transfer of experience in groups of organizations: Implications for
performance and competition. Management Science 48(12): 1517-1533.
Jaspersen JG, Peter R (2017) Experiential learning, competitive selection, and downside risk: A new
perspective on managerial risk taking. Organization Science 28(5): 915-930.
Jeppesen LB, Lakhani KR (2010) Marginality and problem-solving effectiveness in broadcast search.
Organization Science 21(5): 955-1123.
Joseph J, Gaba V (2020) Organizational structure, information processing, and decision-making: A
retrospective and road map for research. Academy of Management Annals 14(1): 267302.
KC D, Staats BR, Gino F (2013) Learning from my success and from others' failure: Evidence from
minimally invasive cardiac surgery. Management Science 59(11): 2435-2449.
Keum DD, See KE (2017) The influence of hierarchy on idea generation and selection in the innovation
process. Organization Science 28(4): 653-669.
Keuschnigg M, Ganser C (2017) Crowd wisdom relies on agents’ ability in small groups with a voting
aggregation rule. Management Science 63(3): 818828.
Klapper H, Piezunka H, Dahlander L (2021) Strategic Behavior in online communities: Evidence from peer
Knudsen T, Levinthal DA (2007) Two faces of search: Alternative generation and alternative evaluation.
Organization Science 18(1): 39-54.
Knudsen T, Marchiori D, Warglien M (2018) Hierarchical decision-making produces persistent differences
in learning performance. Scientific Reports 8(1): 15782.
Knudsen T, Srikanth K (2014) Coordinated exploration: Organizing joint search by multiple specialists to
overcome mutual confusion and joint myopia. Administrative Science Quarterly 59(3): 409-441.
Laureiro-Martinez D, Brusoni S, Tata A, Zollo M (2019) The manager’s notepad: Working memory,
exploration, and performance. Journal of Management Studies 56(8): 1655-1682.
Lazear E (2004) Balanced skills and entrepreneurship. American Economic Review 94(2): 208-211.
Le Mens G, Denrell J (2011) Rational learning and information sampling: On the “naivety” assumption in
sampling explanations of judgment biases. Psychological Review 118(2): 379-392.
Le Mens G, Kovacs B, Avrahami J (2018) How endogenous crowd formation undermines the wisdom of
the crowd in online ratings. Psychological Science 29(9): 14751490.
The Aggregation-Learning Tradeoff
Lee E, Puranam P (2016) The implementation imperative: Why one should implement even imperfect
strategies perfectly. Strategic Management Journal 37(8): 1529-1546.
Lee S (2021) The myth of the flat startup: Reconsidering the organizational structure of startups. Job Market
Lee S, Csaszar FA (2020) Cognitive and structural antecedents of innovation: A large-sample study.
Strategy Science 5(2): 71-79.
Levinthal DA (1997) Adaptation on rugged landscapes. Management Science 43(7): 934-950.
Levinthal DA, March JG (1981) A model of adaptive organizational search. Journal of Economic Behavior
and Organization 2(4): 307-333.
Luce RD (1959) On the possible psychophysical laws. Psychological Review 66(2): 81-95.
Mack DZ, Szulanski G (2017) Opening up: How centralization affects participation and inclusion in
strategy making. Long Range Planning 50(3): 385-396.
Mannes A, Soll JB, Larrick RP (2014) The wisdom of select crowds. Journal Of Personality And Social
Psychology 107(2): 276-299.
March JG (1991) Exploration and exploitation in organizational learning. Organization Science 2(1): 71-
March JG (1996) Learning to be risk averse. Psychological Review 103(2): 309-319.
March JG (2010) The Ambiguities of Experience (Cornell University Press, Ithaca, NY)
Miner AS, Mezias SJ (1996) Ugly duckling no more: Pasts and futures of organizational learning research.
Organization Science 7(1): 88-99.
Mollick E, Nanda R (2016) Wisdom or madness? Comparing crowds with expert evaluation in funding the
arts. Management Science 62(6): 1533-1553.
Myers CG (2018) Coactive vicarious learning: Toward a relational theory of vicarious learning in
organizations. Academy of Management Review 43(4): 610634.
Ober, J. 2009. Mass and Elite in Democratic Athens. Princeton University Press.
O'Mahony S, Ferraro F (2007) The Emergence of governance in an open source community. Academy of
Management Journal 50(5): 10791106,
Padgett J, Powell WW, (2012) The Emergence of Organizations and Markets (Princeton University Press,
Princeton, NJ)
Page S (2007) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and
Societies (Princeton University Press, Princeton, NJ).
Piezunka H, Dahlander L (2015) Distant search, narrow attention: How crowding alters organizations'
filtering of user suggestions. Academy of Management Journal 58(3): 856-880.
Piezunka H, Dahlander L (2019) Idea rejected, tie formed: Organizations’ feedback on crowdsourced ideas.
Academy of Management Journal 62(2): 503530.
Piezunka H, Schilke O (2021) The effect of organizational aggregation structures on individuals’ voting
behavior: An experimental investigation. Working paper.
Posen HE, Levinthal DA (2012) Chasing a moving target: Exploitation and exploration in dynamic
environments. Management Science 58(3): 587-601.
Posen HE, Yi S, Lee J. 2020. A contingency perspective on imitation strategies: When is “benchmarking”
ineffective. Strategic Management Journal 41(2): 198-221.
Puranam P, Alexy O, Reitzig M (2014) What's “New” about new forms of organizing? Academy of
Management Review 39(2): 162180.
Puranam P, Stieglitz N, Osman M, Pillutla MM (2015) Modelling bounded rationality in organizations:
Progress and prospects. Academy of Management Annals 9(1): 337-392.
Puranam P, Swamy M (2016) How initial representations shape coupled learning processes. Organization
Science 27(2): 323-335.
Rahmandad H (2019) Interdependence, complementarity, and ruggedness of performance landscapes.
Strategy Science 4(3): 234-249.
The Aggregation-Learning Tradeoff
Reagans R, Argote L, Brooks D (2005) Individual experience and experience working together: Predicting
learning rates from knowing who knows what and knowing how to work together. Management Science
51(6): 869881.
Reitzig M, Maciejovsky B (2015) Corporate hierarchy and vertical information flow inside the firma
behavioral view. Strategic Management Journal 36(13): 1979-1999.
Reitzig M, Sorenson O (2013) Biases in the selection stage of bottomup strategy formulation. Strategic
Management Journal 34(7): 782-799.
Rivkin JW, Siggelkow N (2003) Balancing search and stability: Interdependencies among elements of
organizational design. Management Science 49(3): 290-311.
Sah RK, Stiglitz JE (1986) The architecture of economic systems: Hierarchies and polyarchies. American
Economic Review 76(4): 716-727.
Schilke O (2018) A micro-institutional inquiry into resistance to environmental pressures. Academy of
Management Journal 61(4): 1431-1466.
Sengul M, Obloj T (2017) Better safe than sorry: Subsidiary performance feedback and internal governance
in multiunit firms. Journal of Management 43(8): 2526-2554.
Simon HA (1947) Administrative behavior: A Study of Decision-Making Processes in Administrative
Organizations (Macmillan, New York).
Sorenson O, Dahl MS, Canales R (2021) Do startup employees earn more in the long run? Organization
Science, forthcoming.
Stan M, Vermeulen F (2013) Selection at the gate: Difficult cases, spillovers, and organizational learning.
Organization Science 24(3): 796-812.
Stieglitz N, Knudsen T, Becker MC (2016) Adaptation and inertia in dynamic environments. Strategic
Management Journal 37(9): 1854-1864.
Surowiecki J (2005) The Wisdom of Crowds (Anchor Books, New York, NY).
Sutton RI, Callahan AL (1987) The stigma of bankruptcy: Spoiled organizational image and its
management. The Academy of Management Journal 30(3): 405-436.
Sutton, R, Barto, A. 1998. Reinforcement learning: An introduction. MIT Press; Cambridge, Mass.
Tarakci M, Greer LL, Groenen PJF (2016) When does power disparity help or hurt group performance?
Journal of Applied Psychology 101(3): 415429.
Tortoriello M, Perrone V, McEvily B (2011) Cooperation among competitors as status-seeking behavior:
Network ties and status differentiation. European Management Journal 29(5): 335-346.
Turco C (2016) The Conversational Firm: Rethinking Bureaucracy in the Age of Social Media (Columbia
University Press, New York, NY).
Vissa B (2010) A matching theory of entrepreneurs' tie formation intentions and initiation of economic
exchange. The Academy of Management Journal 54(1): 137158.
Waldinger F (2012) Peer effects in science: Evidence from the dismissal of scientists in nazi Germany. The
Review of Economic Studies 79(2):838-861.
Woolley AW, Chabris CF, Pentland A, Hashmi N, Malone TW (2010) Evidence for a collective intelligence
factor in the performance of human groups. Science 330(6004): 686-688.
The Aggregation-Learning Tradeoff
Figure 1: Conceptual framework: Inputs to organizational decision making
The Aggregation-Learning Tradeoff
Figure 2: Organizational performance and individual learning across decision-making structures
and organization size
Panel A shows organizational performance across different decision-making structures, averaged across firm size, and
with and without learning. Panel B shows the same but differentiates across organizational sizes. Panel C shows
individual learning across different decision-making structures across all sizes, with Panel D differentiating across
organizational sizes. The figure includes 95 percent confidence interval bars.
The Aggregation-Learning Tradeoff
Figure 3: Individual beliefs mapped to organizational choices
This figure shows the alternative believed to be best by participants (x-axis), compared against the alternative selected
by the organization. The decision-making structure used is plurality, with five individuals. Results are for periods 1-
20 of 10,000 simulations (with learning), allowing for learning-by-participating. Values (and darkness of quadrants)
reflects the frequency-based percentage of each combination.
The Aggregation-Learning Tradeoff
Figure 4: Distribution of individuals’ beliefs, lowest- and highest-performing alternatives
This figure shows, in period 200 (with learning), the distribution of individual-level beliefs for participants under each
of the decision-making structures with size=5 (or 1 in the case of the unitary actor). The dashed vertical line illustrates
the true value of the alternative. The left column shows belief distributions for Alternative 1 (lowest-performing),
while the right column shows belief distributions for Alternative 5 (highest-performing).
The Aggregation-Learning Tradeoff
Figure 5: Share of (A) individuals believing, (B) organizations selecting the highest-performing
Panel A shows, in period 200 (with learning), the percent of individuals that consider each alternative to be highest-
performing. Panel B shows, in period 200 (with learning), the frequency of organizations choosing the various
alternatives, across decision-making structures and sizes. Alternative 1 is the (true) lowest-performing, while
Alternative 5 is the (true) highest-performing.
The Aggregation-Learning Tradeoff
Figure 6: Frequency of correct and incorrect contrarians
The figure shows, in period 200 (with learning), the frequency of deviations in which the organizational choice is
inferior (dark grey) or superior (light grey) to the alternative the participant considers to be highest-performing. We
randomly select one participant in each simulated organization and compare their preferred alternative to that selected
by the organization. The light grey bars, oriented upwards, show incorrect contrarians i.e., the organization’s choice
is superior to the alternative favored by the individual. The black bars, oriented downwards, show correct contrarians
i.e., the organization’s choice is inferior to that favored by the individual. The total bar height (bottom of black to
top of grey) reflects the prevalence of contrarians in a given decision-making structure of a particular size. The
existence of contrarians in period 200 indicates that these individuals either have not learned from other individuals
in the organization or the other individuals in the organization have not learned from them.
The Aggregation-Learning Tradeoff
Figure 7: Numbers of geniuses and organizational performance
This figure shows overall organizational performance for each decision-making-structure of varying sizes in period
200 (with learning). It tracks all instances where an organization had 0, 1, 2, or 3 participants who in period 1 believed
that the lowest-performing alternative is the highest-performing alternative (i.e., geniuses). In the figure these are
depicted by the 0, 1, 2, and 3. Organizations where the organization had 4 or 5 geniuses are not illustrated.
... This finding aligns with past research that has identified behavioral practices used to complement an organization's hierarchical structure. For example, organizations adopt methods to manage deficient or uncertain systematic authority; these include using decision rules to guide behavior from top-down principles (Davis, Eisenhardt, and Bingham, 2009;Piezunka, Aggarwal, and Posen, 2022), allowing local behaviors to emerge from the ground up (Brown and Eisenhardt, 1997;Anderson et al., 1999;Nan, 2011;Moffett et al., 2021), and substituting structural oversight with enforcement via strong cultural norms (Ouchi, 1979;Von Krogh et al., 2012). ...
Collaboration is critical to organizations and difficult when work is distributed. Prior research has indicated that when individuals are distributed, organizations respond by structuring their work to decrease reciprocal interdependence, reduce the complexity of tasks that individuals perform, or accept moderate inefficiencies. Yet in an increasing number of organizations—location-independent organizations—employees are fully distributed, exist without a physical office, and engage in reciprocally interdependent work. To understand how these distributed organizations collaborate, I undertook an inductive multiple-case study. I identify two patterns of collaboration, an asynchronous orientation and a real-time orientation, and reveal the specific enabling practices for each, with a focus on asynchronous-oriented organizations. This research contributes to the distributed work literature by detailing three novel practices that enable effective collaboration for reciprocally interdependent work without geographic or temporal alignment and to the organizational design literature by identifying distinct approaches to distributed collaboration. This study also engages with the future-of-work conversation by providing empirical grounding that enhances our understanding of the theory, boundary conditions, and nuance of the phenomenon of distributed organizations, specifically location-independent organizations.
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Crowdsourcing—asking an undefined group of external contributors to work on tasks—allows organizations to tap into the expertise of people around the world. Crowdsourcing is known to increase innovation and loyalty to brands, but many organizations struggle to leverage its potential, as our research shows. Most often this is because organizations fail to properly plan for all the different stages of crowd engagement. In this paper, we use several examples to explain these challenges and offer advice for how organizations can overcome them.
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Evaluating the attractiveness of startup employment requires an understanding of both what startups pay and the implications of these jobs for earnings trajectories. Analyzing Danish registry data, we find that employees hired by startups earn roughly 17% less over the next 10 years than those hired by large, established firms. About half of this earnings differential stems from sorting—from the fact that startup employees have less human capital. Long-term earnings also vary depending on when individuals are hired. Although the earliest employees of startups suffer an earnings penalty, those hired by already-successful startups earn a small premium. Two factors appear to account for the earnings penalties for the early employees: Startups fail at high rates, creating costly spells of unemployment for their (former) employees. Job-mobility patterns also diverge: After being employed by a small startup, individuals rarely return to the large employers that pay more.
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Crowdsourcing—asking an undefined group of external contributors to work on tasks—allows organizations to tap into the expertise of people around the world. Crowdsourcing is known to increase innovation and loyalty to brands, but many organizations struggle to leverage its potential, as our research shows. Most often this is because organizations fail to properly plan for all the different stages of crowd engagement. In this paper, we use several examples to explain these challenges and offer advice for how organizations can overcome them.
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This chapter provides a review of the scholarly literature on entrepreneurial teams and team formation. It pays special attention to two emerging areas of research that present many promising opportunities for future work. First, the chapter discusses the role of re­source transfer in the context of start-up firms. It argues that an understanding of the an­tecedents and consequences of the founding process would be significantly advanced by more explicit theorizing and effort to empirically identify the specific types of resources entrepreneurial team members bring to start-up firms. It highlights one recent advance­ment in this space—work that has focused on a team’s ability to transfer customer and client relationships from the parent to start-up firms—and provides an outline of open re­search questions in this realm. Second, the chapter provides a primer on a recent methodological advancement—the use of two-sided assortative matching models—that can be applied to entrepreneurial team assembly to alleviate ongoing concerns that team formation is fundamentally an endogenous process. It demonstrates how these models can be applied using a wide variety of founder, cofounder, and early team member attrib­utes, including an individual’s ability to transfer customer relationships. Importantly, it proposes that synergies emerging from the use of two-sided assortative matching models to study a broader set of team member attributes that include resource transfer will open promising new avenues for future research.
Online communities that typically defy traditional forms of hierarchy often rely on lateral authority delegated to peer evaluators. While peer evaluations are supposed to be meritocratic, we theorize that people engaged in peer evaluation are often motivated to behave strategically. Processes that make evaluations transparent also motivate actors to consider how their evaluation of a peer will be perceived, and how they themselves will be perceived and evaluated in turn. This creates a dilemma with respect to evaluating peers negatively. Evaluating at least some peers negatively is important to be perceived as an honest, capable member of a meritocratic community, yet negative evaluations can provoke retaliation. We theorize that strategic actors reserve their negative evaluations for peers who are unlikely or unable to retaliate. We find support for our arguments in a unique dataset of peer evaluations in elections for Wikipedia administrators in 2003–14, plus detailed qualitative interviews with 24 Wikipedia peer evaluation participants and administrators. We elaborate our results with implications for research on peer evaluation in online communities and beyond as well as on novel forms of organizing where meritocratic, peer-based ideals persist.
There has been an ongoing debate over whether start-ups should be “flat” with minimal hierarchical layers. To reconcile this debate, this paper distinguishes between creative and commercial success (i.e., novelty vs. profitability), and examines how these outcomes are variously influenced by a start-up's hierarchy. This study suggests that while a flatter hierarchy can improve ideation and creative success, it can result in haphazard execution and commercial failure by overwhelming managers with the burden of direction and causing subordinates to drift into power struggles and aimless idea explorations. I find empirical support for this trade-off using a large sample of game development start-ups. These findings offer one resolution to the debate by sorting out the conditions under which hierarchy can be conducive or detrimental to start-ups.
Research summary We examine how university entrepreneurship programs affect entrepreneurial activity using a unique entrepreneurship‐focused survey of Stanford alumni. OLS regressions find a positive relationship between program participation and entrepreneurship activities. However, endogeneity hinders causal interpretation. We utilize the fact that the entrepreneurship programs were implemented at the school level. Using the introduction of each school's program as an instrument for program participation, we find that the Business School program has a negative to zero impact on entrepreneurship rates. Participation in the Engineering School program has no impact on entrepreneurship rates. However, the Business School initiative decreases startup failure and increases firm revenue. University entrepreneurship programs may not increase entrepreneurship rates, but help students better identify their potential as entrepreneurs and improve the quality of entrepreneurship. Managerial summary Recently, many universities have developed programs to promote entrepreneurship. However, relatively little is known about the impacts of such university initiatives. In this article, we examine the two major initiatives that were established in the mid‐1990s—the Stanford Center for Entrepreneurial Studies at the Business School and the Stanford Technology Ventures Program at the Engineering School. We find that the Business School program had a negative to zero impact on entrepreneurship rates and participation in the Engineering School program had no impact on entrepreneurship rates. However, the Business School initiative decreased startup failure and increased firm revenue. University entrepreneurship programs may not increase entrepreneurship rates, but help students better identify their potential as entrepreneurs and improve the startup performance.
This paper studies how cognitive and structural antecedents affect adaptation to disruptive innovations. We do so by analyzing how video game firms adapted to the free-to-play business model around the period of disruption (2012–2015). Our data set (which contains 461 firms, collectively employing 83,157 individuals) allows us to characterize each firm’s organizational structure and each employee’s experience profile; it also captures the performance of firms under the existing and new technological regimes (that is, firms that do and do not adopt the disruptive innovation). We show that adoption, implementation under the existing regime, and implementation under the new regime are affected by cognitive and structural antecedents in different and often opposite ways. We also point out conditions under which cognitive and structural antecedents can compensate for each other. Overall, our study contributes to a better understanding of how firms should organize to face disruptive innovations.
How should decision-making be organized in entrepreneurial teams when founders exhibit confidence biases? New ventures are commonly founded by teams of entrepreneurs, who must employ a decision-making structure that implicitly or explicitly defines how individual beliefs are aggregated into team decisions. We consider this issue through the lens of organizational economics that focuses on decision-making governance. Using a computational model, we consider three archetypal decision-making structures: partnership voting, a boss with employees, and a buyout option (partnership convertible to boss structure). We highlight the conditions under which partnership voting is an effective means of governing market entry and exit decisions when teams’ decision-making is informed by efforts to learn about the merits of uncertain opportunities. The promise of partnership voting is realized when entrepreneurs are either unbiased or optimistic about their likelihood of success. Partnership voting is problematic when entrepreneurs differ in their biases or respond too rapidly to new information, in which case a buyout option is better. From a policy perspective, we show that confidence biases may be managed by selectively matching the decision-making structure to entrepreneurs' biases, and that doing so may substantially improve the performance of new ventures.