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The Role of Conviction and Narrative in Decision Making under Radical
Uncertainty.
David Tuckett and Milena Nikolic
i
(UCL Centre for the Study of Decision-Making Uncertainty)
A general framework is proposed to broaden existing decision-making theory to
enable it better to understand and analyse how subjectively means-end rational
actors cope when the assumptions of traditional optimising models fail to hold. The
framework, termed Conviction Narrative Theory (CNT), focuses on the power of
narrative and emotion to combine to facilitate action. In CNT agents are able to act
because they draw on cognitive and affective resources to form preferred narratives
of the outcomes of their planned actions. Such narratives, conviction narratives,
establish preference and enable action readiness specifically by evoking feelings of
approach and avoidance. They play a necessary but not sufficient role in decisions
to act under radical uncertainty, however these decisions turn out. Through
developing conviction narratives, actors, in objectively uncertain conditions,
become certain enough to act, despite the possibility of serious loss. CNT integrates
many research findings over disparate domains. We then introduce the concepts of
Divided and Integrated States to represent two different emotional contexts in
which narratives are evaluated. Finally, we report two algorithmic techniques that
have proved useful for identifying these states and linking them to economic
outcomes, such as the outbreaks of financial instability observed prior to the
financial crisis.
Key Words: Action; Conviction; Decision-Making; Adaptive Heuristics; Embodied Cognition;
Feelings; Narrative; Pattern Recognition; Prediction; Simulation; Uncertainty; Optimism bias.
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The Role of Conviction and Narrative in Decision Making under Radical
Uncertainty.
1. Introduction
In the complex, dynamic and interconnected world in which we live,
consequences cannot reliably be foreseen. This means that many important
decisions are made in radical uncertainty – that is, in a context in which decision-
makers cannot know all the relevant alternatives available to them or what the
consequences will be of the ones they choose. Innovation is one driver of such
radical uncertainty, producing a potentially unstable environment on which to act.
However, even in an apparently stable environment, unforeseen consequences
regularly follow from new complex constellations of interdependent events. An
added difficulty is that it is often very difficult to decide whether a particular
decision brought about subsequent events.
Radical uncertainty is the context for most decisions made by leaders in
government or corporations and for significant individual decisions such as buying
a house, getting married, saving for a pension or making a career move. Yet most
research on decision-making has avoided taking this into consideration, choosing
instead to model optimal decision-making as dependent on correctly forming
probabilities to estimate the future states of the world contingent on a proposed
action. Classical decision-making theorists and economists, for example, restrict
themselves to situations of (known) risk or transform radical uncertainty to risk
in order to apply the probability calculus (for example, Savage, 1951; Friedman,
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1976; and see Bewley, 2002). Even critics of neoclassical economics, such as
behavioural economists, have not dared to develop an appropriately adapted
normative alternative. The widely popularised and influential heuristics and
biases approach (Kahneman et al, 1982), for instance, is only descriptive. It labels
deviations (biases, framing errors) from a normative approach, often suggesting
they are caused by defaults to a more automatic and less reflective system
(Kahneman 2011). Consequently, the deeper question of what it means to be
rational when the assumptions of traditional optimising models fail to hold has
not been confronted. Little effort has been made either to model how people cope
in such situations, or to understand the implications (see, Simon, 1955; King,
2016). This may be a highly significant omission. For instance, the failure to
incorporate radical uncertainty into economic and finance models has been one
factor held responsible for the recent economic and financial crisis (Gigerenzer,
2014, Kay, 2015, King, 2016).
How do people manage to act when optimisation is not possible and they
are required to cope with radical uncertainty, and what are the consequences?
This article offers a new social-psychological theoretical framework to model
decision-making in radical uncertainty. We refer to this framework as conviction
narrative theory (CNT). Conviction narrative theory postulates that the central
tool for understanding and forward planning used by actors to make decisions
under radical uncertainty is narrative construction within their social
environment. Narratives are inherently temporal. Their plots link action and
outcome (Brooks 1984, Bruner, 1991). Conviction is an emotional resource that
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readies action. Conviction narratives enable actors to draw on the beliefs, causal
models and rules of thumb situated in their social context to identify opportunities
worth acting on, to simulate the future outcome of the actions by means of which
they plan to achieve those opportunities and to feel sufficiently convinced about
the anticipated outcomes to act. We see such narratives as founded on biologically
and socially evolved coping capacities that allow individuals to prepare to execute
particular actions even though they cannot accurately know what the outcomes
will be. They also provide an easy means for actors to communicate and gain
support from others for their selected actions as well as to justify themelves.
Our argument is organised as follows. In section 2, we clarify what we mean
by radical uncertainty and use data from a study of asset managers to illustrate
the issues faced when it is present. In section 3, we outline the CNT framework
and in section 4 we discuss the processes that lead particular narratives to be
selected. Finally, we discuss some of the new insights and predictions made
possible by this approach and some new research lines that the framework opens.
2. Coping with Radical Uncertainty
We use the term radical uncertainty to emphasise an uncertainty so
profound that it is impossible to represent the future in terms of a knowable and
exhaustive list of outcomes to which we can attach probabilities
1
(Keynes, 1937,
Kay, 2015, King, 2016,). The future will surprise.
1
Related terms for the same phenomena are ‘ontological’ or ‘ontic’ (Lane and Maxwell, 2005; Petersen, 2006), “deep”,
“Knightian’ (Knight 1931), or ‘model’ uncertainty (Chatfield, 1995). It is a form of uncertainty in which the system model
generating outcomes and the input parameters to the system model are not known or widely agreed on by the
stakeholders to a decision (Lempert, 2002). In psychology, it has sometimes been referred to in a general way as
‘ambiguity’ and it could equally well be discussed as “ignorance”.
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To illuminate the critical issues confronting decision makers faced with
such uncertainty we will extract some details from an influential study of 52 asset
managers interviewed in 2007 and 2011 (Tuckett, 2011; 2012; Tuckett and Taffler,
2013; Chong and Tuckett, 2015). Asset managers are among the most highly
remunerated members of society. They search for profitable opportunities for
investment that have not been exploited (Kay, 2015 p 70). Their decisions
determine the allocation of the world’s savings between possible economic
activities – although the evidence from academic finance is that individually they
do not consistently perform better than chance (for example, Barras et al. 2010;
Busse et al. 2010; Fama & French, 2010; Wermers, 2011 and see Kahneman,
2011).
The task is to seek and hold assets today that will be worth relatively more
than today in the future. To find such assets, managers must make two linked
judgments. First, they must interpret a massive quantity of data and anticipate
what will happen to the underlying entities in future (to the assets they hold and
to other comparable assets). Second, they must anticipate how their estimates of
the future compare to other asset managers’ estimates. The future value of assets
depends on not only on what will happen to the entities but also on how other
people will price them when you come to sell in the future. Markets are social.
Large sums are involved and for those making decisions and their clients the
decisions are “major”.
2.1. Tristan Cooper
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Tristan Cooper was one of 52 very highly successful asset managers studied.
He was trying to decide whether to buy equity in a very large construction
company valued at over two billion Euros.
The company was doing some “very interesting things” which Cooper said
made him think that it would be highly profitable and pay good dividends making
it more and more valuable in the future.
First, the company is identified as not ordinary.
He said that at the time he was thinking to invest the stock was cheap
because one of the company’s subsidiaries in one European country had run into
some difficulty, causing two profit warnings. Some investors no longer trusted the
management and were selling off their holdings.
Second, the decision involves choosing between two incompatible
possibilities – is the asset cheap due to past difficulty or because there are more
fundamental problems? If the former, it will recover. If the latter, it may not.
To decide why it was cheap Cooper went to visit the company and sat down
with the Chief Financial Officer (CFO) “to try and get a sense of what kind of guy
he is”. He concluded in a meeting that the CFO came “up with a very decent
explanation as to why they had screwed up” and gave a number of reasons.
Third, Cooper forms a view of the future based on an explanation he develops
to account for the present situation and the assessment he makes of its accuracy.
“From a valuation standpoint”, Cooper explained, “if you're a construction
company worth 2 billion euro then if you have to write off 60 million, once, it
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shouldn’t matter a lot. Your earnings for the year are going to be destroyed, but
you're only taking off this much of your market cap so the stock should only drop
3% or something that day, and then you forget about it.” The share price should
recover.
Fourth, Cooper’s explanation produces a logical narrative to explain why
the asset is cheap now and why this will not be the case in the longer term.
He bought the stock in May. And then along “comes the month of November
or something, and they have another big [problem] in that country, and I think,
wow, the guy was coming up with an explanation that I found reasonable, I thought
he thought the situation was under control, but now it’s not.” They really do have a
forensic accounting problem, etc.
Fifth, decisions have to be maintained over time. Uncertainty creates
ongoing conflict and doubt and threatens commitment. Experiencing uncertainty,
Cooper now questions his evidence and his logic.
“Nothing had changed. I should have said, fine, that is another just 50
million… From a strict mathematical standpoint…it doesn’t matter.”
Sixth, he tries to hold on to his logic.
Cooper elaborated that he had hung on at first “probably because I trusted
the guy, and I thought I was smarter than everyone else”.
Seventh, his logic at this stage is supported by trust in his source of evidence
and his judgment of it.
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Then “I saw who was selling and not buying and I just couldn’t hang on
anymore. “The stock was down like 14%; I just sold it.”
Eighth, the logical account underlying his decision had to be fashioned in a
context of social pressure and shifting emotion. Was his view accurate or not?
Decisions are made in a social context and Cooper could see what significant others
were doing (and specifically which others). He could see, and had to worry about,
the implications of a falling price. The combination made him abandon his trust
in his view and change his vision of the likely future. He sold and took a loss.
However, “The stock has long ago made back what it lost and has been a
super star since then. So my valuation case was there, it was right, except I got
distracted… and that's happened to me before.”
Ninth, therefore, Cooper eventually concluded that there had been nothing
wrong with his initial logic or his approach to evidence. With hindsight, his
original thesis was actually proved correct, but he had not been able to maintain
his view under the emotional experience of uncertainty.
2.2. Discussion
The example illustrates five aspects of the decision context faced by
decision-makers of this sort. The context has novel, out of the ordinary features.
There were at least two plausible options with no clear way to choose. Calculation
could explore options but not decide between them. Anticipating and
understanding what others would do mattered. A principle used previously looked
applicable –temporary prejudice towards a company created by events offers an
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opportunity for the unprejudiced to make money at the expense of the prejudiced,
if the problems should prove temporary. The role of emotional experience
(specifically conviction) is particularly evident, probably because counterfactuals
to the chosen outcome narrative soon emerged and created uncertainty. Cooper at
first tried to maintain his narrative in the face of doubt by holding on to the logic
backing his decision. At the same time, he could see that others who were
significant to him within a relevant social group were selling the stock. His anxiety
that he was wrong led him to abandon his narrative. In fact, his original idea was
sound but his narrative could not withstand the pressure of doubt under when the
outcome was radically uncertain.
Cooper’s dilemma demonstrates a core feature of the nature of radical
uncertainty. It continues to create conflict about what to do even after the initial
decision – as new information or new interpretations of information are considered
should the decision and its supporting narrative be embraced or avoided, held or
abandoned? Cooper and other actors studied
2
who make decisions to act in
radically uncertain contexts use narratives to structure and interpret the
significance of very large quantities of information, including their own emotional
responses. They had no means of telling whether the calculations they undertook
would prove to be accurate representations of the future with any measurable
certainty. Their crucial achievement is that with only equivocal data they can
2
For details of the methodology supporting the asset manager study see Tuckett (2012) and Tuckett and Taffler (2013). Lane and
Maxwell (2005)’s ethnographic account of the development of Echelon, a US start-up company is a further example. They manufactured
electronic chips with integrated software and successfully gained market share in the market for electronic control of air conditioning,
heating and other distributed devices. Describing the ontological uncertainty they faced in an ethnographic study, Lane and Maxwell argue
that believing their narrative of how things would work was crucial.
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create a strong enough feeling of certainty about the trajectory of the future they
predict to conclude that the preferred action would in fact work out to their
advantage - that they can wait for the narrative selected to unfold
3
. In this way
and so long as new events do not force new interpretations and new narratives,
such agents make themselves dependent on their predicted outcome and act even
at the risk of significant loss.
3. The CNT Framework.
Faced with situations in which probabilities are not available, such as those
described, how should we model how loss-averse individuals cope in a means-ends
rational way? In CNT we suppose that in order to act when outcomes are
(objectively) uncertain, individuals must (subjectively) find ways to envisage that
some gain will accrue. The subjective confidence to act is enabled by creating or
adhering to a preferred narrative of how a planned action will have a particular
outcome, which we call a conviction narrative.
We use the term narrative to refer to a general form of mental organization
(Baumeister and Masicampo, 2010) that allows experience to be ordered in time
(Graesser, et al 1980; Bruner, 1991) into what we can think of as manageable
“chunks” (Miller, 1956). Narratives are recognised to connect goals and plans
(Pribriam et al, 1960) to predict or reveal outcomes (Brooks, 1984). In doing so
they necessarily contain explanatory causal dynamics (Baumeister and
Masicampo, 2010; see also, Beach, 2010; Schank & Abelson, 1977). Like speech
3
Subjectively, actors themselves may be so successful at avoiding t he existence of alternative but possible narratives that t hey have little
awareness this is really what they are doing. In one sense this describes the conviction with which normative approaches to decision-making
are applied to real life, such as risk assessment or financial trading (see Taleb, 2004; Kay, 2015).
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and sentences, counting, logical reasoning and causal inference, narrative chunks
can be combined into more and more complex combinations, creating more and
more complex forms of narrative.
We propose that conviction narratives, by representing how actions lead to
imagined outcomes, facilitate action under radical uncertainty because they create
a particular quality of felt, embodied, knowledge of the outcome. In this way, they
establish action preference and enable action readiness. Specifically, they permit
human actors:
1. To make sense of the situations they are in, which means to identify an
opportunity for action on the basis of observations about a pattern with an
implicit causal explanation and predictive potential;
2. To form alternative representations of the future outcomes of actions, so as to
predict their subjective impact;
3. To select a narrative of the anticipated outcome that creates a sufficient feeling
of accuracy about its predictions to enable and support action, making it
possible to sustain a commitment to the course of action even though there is
a risk of loss;
4. To communicate about their actions and justify them in social contexts.
Although we have separated these four functions of narratives in decision-
making for expository purposes, it is unlikely that they are in fact separate.
Pattern recognition, simulation and feelings of accuracy tend to go together in a
particular social environment so that directions of causation may be moot. For
instance, several current models suggest that most of our brain functions seem to
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have a predictive nature, so that even our perception of the present seems to entail
a modelled prediction already informed by prior experiences (Clark, 2013; Friston,
2003; Pezzulo et al., 2015; Greve, 2015; Wolpert et al, 1995). More generally, vision
involves reconstructing causal history from static shapes (Chen and Scholl, 2016)
and pattern recognition (for example, diagnosis in medicine or the identification
of the purpose of a person passing by with a newspaper in the early morning
(Garfinkle, 1967)) tends to imply both underlying causal explanation and future
prediction.
3.1. Identifying Opportunities for Action by Observing Patterns
In the examples above, the asset manager, Cooper, had to interpret data
and its relevance to his subjective plans. This data was not given to him in the
way he would have received it in a laboratory experiment. Rather, he started out
with a general high-level conviction narrative as to how his actions could be
successful (Chong and Tuckett, 2015) defined by himself and the institutional
context in which he worked. To act he had to identify specific opportunities and to
search for, attend to and examine information of relevance, while at the same time
knowing that he did not have enough data to draw clear and unequivocal
conclusions. He actively constructed, interpreted and modelled current realities in
terms of a number of rules of thumb that had predicted implications for the
outcome of their actions.
In the example given, he came across a situation he interpreted as a
temporarily “prejudiced” stock, meaning one suffering from rumours. The
underlying causal model is that stocks whose price is depressed by rumour will
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rebound. Implementing a rule of thumb of this sort is a typical example of the way
asset managers faced with virtually limitless piles of data identify cues to action.
Such decision rules organise their world into a stock of opportunity narratives that
function as adaptive heuristics (Gigerenzer and Gaissmaier, 2011; Gigerenzer,
2014)). In fact, Cooper’s choice fitted the second of eleven such decision rules
identified in the asset manager study mentioned (Table 1)
4
.
Table 1: Search rules for identifying opportunities used by 52 Active Fund Managers
1. Look for complex companies that are hard to understand and have received little market
attention but which you can show are undervalued based on past performance
2. Look for shares hit by possibly exaggerated rumours (e.g. of impending litigation or
compensation pay-outs).
3. Look for companies sitting on “piles of cash” as they often find ways to return this cash
to the shareholders while isolating losses in spin-offs.
4. Look for market-leading companies identified as likely to benefit substantially from
regulatory change.
5. Look for companies in sectors in fast growing regions in which there will be a growing
inability to meet their own level of demand (e.g. the need for gas supply to power stations).
6. Look for related demands (e.g. drilling, building pipelines or importation of gas) and buy
companies that provide these services.
7. Look for and evaluate the sentiment on management teams within companies.
8. Look for companies with strong regional market solutions that appear undervalued.
9. Look for low value companies (due to market perception) in sectors that do badly when
interest rates rise.
10. Look for government/industry funding partnerships - often treated as off-balance sheet
funding for governments.
11. Look for the typical “sound investment” that “ticks all the boxes” (new listing, big
company, barrier to entry exist, good margins, free cash flow, good management etc.)
Decision rules like the ones listed in Table 1 classify situations by
identifying opportunities with predicted outcomes and providing guidance as to
the further investigation required. They make the situation intelligible and
actionable and so turn the ongoing complexity of the world into something
comprehensible and predictable which can be a springboard for action (see Weick
et al, 2005). An early example in sociology was Sudnow’s (1965) concept of “normal
4
This table was initially constructe d by Nick Jewell of HSBC’s Innovation Centre from an analysis of all the narratives in Tuckett (2011).
Jewell was attempting to build a machine learning classification of decision-making narratives to develop an automated computer support
algorithm for asset managers.
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crimes” – allowing the legal system to process criminals quickly (see also Beckert,
2011; Berger and Luckman, 1967; Garfinkle, 1967; Goffman, 1956; Holmes, 2009).
3.2. Simulation
The opportunity identification function of narrative works simultaneously
with a second, simulation function. The latter allows the construction and
rehearsal of any number of imagined future outcomes of planned actions and at
the same time enables these to be evaluated and compared. Cooper sketched out
scenarios that might follow depending on whether he held on to or sold his
investment, consequent upon various imagined events. Likewise, the executives of
Echelon described by Lane and Maxwell (2005) developed alternative narratives
to weigh up the benefits of the imagined outcomes of different actions before
deciding which to adopt.
Whether narratives are formed through “telling” them to oneself, reading
or listening to them or by telling them or writing them for others, they draw on
and express the human capacity for foresight and simulation. They permit the
future to be imagined, expressed, communicated, thought about, “dreamt up”.
Action can then follow to bring the future about.
We think of narrative simulations as built on the neural and cognitive
processes underlying both past pattern recognition and future pattern prediction.
Although the detailed workings of these processes remain debated topics in
contemporary cognitive neuroscience, it is already clear that the processes
underlying the ability to travel mentally backwards or forwards in time are rather
similar (Suddendorf & Corballis (1997). An extensive review of the relevant
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literature concludes that simulation is a goal-directed process that involves
imaginatively placing oneself in a hypothetical scenario and exploring possible
outcomes, which depends on the same neural machinery as remembering past
events. The primary function of what Schacter et al (2008) call the “prospective
brain,” is to store experiences to anticipate future events. We suppose that feelings
associated with the various chunks (i.e. bits of pattern recognition, categorisation,
available adaptive heuristics, sense making and narrative typification) we have
mentioned above are stored in the brain. We can then conceive them as drawn on
to build a conviction narrative that supports action under uncertainty, fusing
prediction about outcomes with action to bring them about.
The idea that narratives play an important role in goal-directed reasoning
(e.g. Gollwitzwer, 1993; Osman, 2012; Szpunar, 2010; Taylor and Schneider, 1989)
is well attested. Narrative simulations are also used in stress testing and foresight
plausibility exercises to help actors to think through possibilities, to test their
likely reaction, to determine the likely outcome of actions and the risk that things
can go wrong (Wilkinson et al, 2013).
3.3 Supporting Action Emotionally.
In a context of radical uncertainty, simulations of potential actions can potentially
produce conflicting outcomes, pleasurable and not pleasurable, with the additional
possibility of the entirely unforeseen. Crucially, the context potentiates inevitable
conflicts, both at the time of the decision and for as long as outcomes remain
uncertain. While these conflicts might create curiosity, excitement and an urge to
act, they can also trigger aversion and inhibit action.
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Conviction narratives play the crucial role of managing approach-avoidance
conflicts so that action can be readied. While narratives (that make sense, make
predictions and simulate the outcomes of action as discussed) are being thought,
told, read or heard by human actors, they create a particular quality of subjective
“knowledge” about the outcome of a plan.
Knowledge in this view is both cognitive and affective, based on felt
experience. It is a special sense of knowing that operates through multiple levels
of consciousness and so-to-speak as “happenings” in neural architecture. For
instance, we envisage that as a decision-maker rehearses a narrative through
thinking, listening or reading, the component elements or narrative chunks within
it produce a felt experience of emotions. What we might think of as “yes that makes
sense”, or “no, that doesn’t seem right” emotional experiences occur as the various
narrative elements are encountered. This is a dynamic process and insofar as
actors are in a state of mind to notice negative feelings about possible outcomes,
they are motivated to respond to them. They can explore influences and resolve
doubts – perhaps by finding a narrative chunk able to allay a growing anxiety
about an outcome (see Chong & Tuckett, 2015) or by changing their preferred
action entirely.
The supportive function of narrative we propose focuses attention on the
positive role of emotion in decision-making. To act under radical uncertainty, the
decision-maker must leap into the unknown. Figure 1 contrasts the role of
emotional processes in CNT with its role in most other existing models. Model A
represents a simplified model of decision-making in which decisions are mediated
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by the separate operation of emotional and cognitive processes on action, along the
lines of the many dual process set out to explain human bias (e.g. Evans, 1984;
Sloman, 1996; Kahneman & Frederick, 2002). In such models, affects (such as
optimism or loss aversion) may be evoked automatically or otherwise in the
decision-making process, but they are not an essential component. Rather, their
role is as a bias or hindrance to reflective thought.
CNT makes use of the growing weight of evidence that the cognitive system
evolved from more primitive brain
functions to support action in
specific situations, including
social interaction. It is “the
outcome of interaction between
perception, action, the body, the
environment and other agents,
typically during goal
achievement” (Barsalou, 2008 p
619). The characteristic of
narrative in such a system is that it enables human decision-makers to combine
cognitive and affective experiences to create a sense of verisimilitude (Bruner,
1985).
Model B depicts this role of emotion in the CNT model. Here, cognitive and
emotional processes, activated in an individual’s local social context, interact in a
circular fashion to produce a feeling of accuracy (the condition for action under
Figure 1: Two Ways of Conceiving the Role of Emotion in Decisions to
Act.
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radical uncertainty). Approach emotions evoked in the particular narrative
dominate so that the planned action feels convincing. In this way, conviction
narratives support action and go on doing so unless updated.
Feelings have an evolutionary purpose in maintaining homeostatic control
(Damasio & Carvalho, 2015) and approach-avoidance (the pleasure-unpleasure
series) is the keynote of affective consciousness located deep inside the brain (e.g.
Solms, 2013). One pathway (approach) in human architecture “is responsive to the
opportunities in the environment” and the other (avoidance) is “responsive to the
dangers” (Carver & White, 1994. p305). They are separate dimensions which when
evoked do not cancel one another out (Kennis et al, 2013, Gray, 1994, Fowles, 1988,
Knowles & Linn, 2004). From these foundations, we can then think of narrative
simulation as creating states of the body - deep subjectively experienced states of
well-being or discomfort evoking approach or avoidance (Damasio and Carvalho,
2013, Panksepp, 2014, Pezzulo et al, 2015). In making and then reversing his
decision Cooper struggled with approach and then avoidance. When a particular
simulation (involving particular narrative chunks and locally adaptive rules of
action) triggers internal conditions judged to favour survival and reproductive
success so that they feel “good”, rather than those signalling danger, which feel
“bad”, that narrative becomes dominant.
The implication is that actors experience narratives as accurate emotionally
as well as cognitively. While simulating outcomes an actor imaginatively projects
his body into the future to anticipate the experience of his future self as well as
that of the others represented. To feel there are good grounds to act a decision-
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maker must, overall, be able to repel doubts that may come up. Here, the ability
of human actors to draw on feelings of conviction provides an advantage
unavailable to a computer generating only scenarios. While a computationally
competent outside observer may be unable to identify secure grounds to support a
particular narrative of the future in radical uncertainty and so to commit to a
particular decision, a human decision-maker can feel sufficient conviction to act.
Narratives create experienced rather than just abstract “knowledge” and this
characteristic makes them particularly well suited to the task of providing support
for action, founded on an emotionally coloured and subjective feeling of “knowing”
what will happen.
3.4 Communication and Gaining Co-Operation
Action often involves teams. A fourth relevant feature of conviction
narratives, therefore, is that they serve social communication functions, providing
what might be called ‘logics of action’ (DiMaggio, 1997) in a given domain. Mar
and Oatley (2008) have shown some of the detailed ways narratives serve to
communicate complex messages in a comprehensible way. Because emotion is
easily shared conviction narratives seem likely to be particularly useful to co-
ordinate social support for strategies within organizations or societies. Their
framing and development in social contexts (in development of a corporate plan,
for example) will also anticipate the need to use them for those ends.
4. Generating Conviction
We hypothesise that a preferred narrative for action emerges through an overall
appraisal of approach and avoidance feelings evoked by every chunk of the
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narrative predictions about outcome that form it. For instance, the credibility of
the information contained in it, the implicitly and explicitly held beliefs that
govern expectations, the decision rules guiding inference, search and checking
that are adopted, the explanatory causal models used in simulating the outcomes
of action, the various action tools selected, etc. A preferred and conscious narrative
of the outcome of action emerges from such an evaluative process, in part explicit,
but much of it implicit and automatic, to evoke a state of action readiness
5
.
We hypothesise that the approach/avoidance feelings evoked by the
different elements are the crucial drivers. Therefore, for example, opportunities
identified by the decision rules in Table 1 are what Chong and Tuckett (2015) call
attractors- tried and tested tools for identifying profit opportunities that evoke
pleasure. Other procedures that asset managers reported enabled them to manage
thoughts (and so avoidance feelings evoked) about things going wrong –doubt
repellers (Chong and Tuckett, 2015). Managers undertook (sensible) routine
activities to repel doubt and so hold on to pleasure. For instance, they used rules
of thumb to reassure about the limits to loss, to search company staff histories for
reliable risk management, and even to test whether they, the decision-makers,
might be reaching judgments influenced by emotional overconfidence (Chong and
Tuckett, 2015 p19). Cooper knew he needed to feel trust in the company
management. He therefore undertook a visit to the chief financial officer and
5
The role of emotion in creating what has been termed “ action readiness” has been well established (e.g. Arnold, 1960; James, 1890;
Lazarus et al, 1980; Schachter & Singer, 1962; Scherer, 1984). Processes of subjective evaluation (automatic and unconscious and/or
controlled and deliberate) occur and generate emotional arousal and action readiness throughout an individual's encounter with significant
events in the experience of the environment (Scherer et al, 2012).
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through a searching interview using “tough” questions tested the remaining
potential for fraud.
Decision rules situated in decision contexts are examples of adaptive
heuristics (Goldstein & Gigerenzer, 1996, Gigerenzer, 2014) or simple rules (Sull
and Eisenhardt, 2015) – i.e. verbal rules for making decisions that have built up
in a particular context and are currently accepted as good enough for a particular
task. They “tell” an actor what to do to reach his goal and comprise subjectively
recognized and familiar patterns available in the environment and stored in
memory (e.g. Simon, 1955; Klein, et al, 1986). Action is initiated because situations
are quickly recognised as fitting a familiar pattern (see e.g. Kahneman & Klein,
2009; Sinclair & Ashkanasy, 2005) or as giving cause for confidence (e.g. Koriat &
Levy Sadot, 2001; Simmons & Nelson, 2006).
As we see it, rules that are situationally valid feel good. They are woven into
conviction narratives based on implicit models that may have become normative
and automatic. They are selected because in that context they evoke approach
emotions and so indicate to an actor how to cope with a situation in a reasonable
way: what information to search for and what else to do before taking action. Such
rules exist within (implicit) models of the underlying situation. Cooper (like other
investors (e.g. Dreman, 2012)) models asset prices as influenced by fear. Profit
opportunities are there provided he stays “coldly analytic” and uses his available
tools to repel doubts. Decision rules follow from implicit models of ways the world
works that have worked before and feel good.
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Figure 2 shows how the elements that go into a conviction narrative work
together to produce action. We will illustrate with the example we have been
using.
The idea is that Cooper’s decisions start from a general conviction narrative
(1) underpinning his sense of his own skill in his role (Chong and Tuckett, 2015).
It provides a prototype for ways to identify profitable opportunity (2). In his case,
Cooper had a high-level “value investing narrative” that led him to seek stocks
whose price was down. One specific opportunity is given wherever there is
temporary reputational damage. Other opportunities he and others looked out for
are in the 11 types identified in Table 1. They each form decision rules that enable
them to identify opportunities and to determine what additional information
needs to be sought to test if they are “really” there.
Heuristics, simple rules, more or less implicit models and beliefs (3) inform
the components built into an outcome narrative. In Cooper’s case, they are drawn
Figure 2. Selecting a Conviction Narrative and using it to take action.
Conviction
Narrative
(approach+
+/- doubt repellors)
= +>-
(6) Local Social Environment
(3) Heuristics,
Simple Rules,
Models,
Conventions,
Repertoires,
Beliefs, Etc.
(4) Trusted Source
Components
(5) Presentational
Components
Action
(2) Opportunity Identification
(1) High Level Narrative generating
Prototype Conviction Narratives
Approach Emotions
Evoked (-)
Avoidance Emotions
Evoked (-)
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from his stored experience in his environment. He applied them to his information
searching and checking - the outcome of which would be influenced by his
subjective impression of the ways the information is presented (5) as well as stored
ideas he has about trusted sources (4). Each element of the process to which he
attends evokes approach and avoidance emotions, so that if action is to take place,
an eventual conviction narrative about its outcome emerges to support it in
pursuit of his goals.
Much research already shows how evidence is attended to and evaluated
differently according to various presentational features so that we can hypothesise
factors likely to influence narrative appraisal. For instance, studies show that
sources considered more legitimate, trusted, credible, competent or important
carry more weight (Suchman, 1995; Tormala et al 2009; Malshe, 2010; Birnbaum
et al 1976; Birnbaum & Stebner, 1979; Fishbein & Ajzen, 1975; Boninger et al,
1995). Similarly, information that is easier to process, or which has been obtained
with greater effort, or is thought more complete, will be judged as more accurate
(See, Alter & Oppenheim, 2009; Tversky & Kahneman, 1973; Petrova & Cialdini,
2005; Whittlesea, 1993; Song & Schwartz, 2009; Bizer et al, 2006; Holland et al,
2003; Petrocelli et al., 2007; Haddock et al, 1999; Wan et al, 2010; Smith et al.,
2008; Barden & Petty, 2008; Chaiken, 1987; Petty & Cacioppo, 1986; Petty &
Krosnick, 1995).
The components in (3), (4) and (5) will be drawn from and so vary with the
norms and interactions within a decision-maker’s local social or institutional
environment (6). For example, information relevant to treatment compliance and
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accurate understanding in medical consultations is more successfully conveyed
when doctors’ and patients’ explanatory models of a diagnosed condition are either
initially congruent or are brought into congruence through communication
(Kleinman 1978; Tuckett et al, 1985; Tuckett, 2015 and see Furnham & Henley,
1988; Faro et al, 2010; and Tsai & McGill 2011). More generally, the sociologist Di
Maggio (1997) has analysed the links between culture and cognition to suggest
that the perceived relevance and accuracy of narrative elements will both
influence and be influenced by social interaction and social location (see also,
Granovetter, 1985). Research on attitudes also suggests that narratives that
contain elements perceived to be congruent with actors’ ways of thinking and
ascribing causality have a better chance of being perceived to be accurate than
those that seem “foreign” (Rucker et al (2014; Tormala, et al (2011).
5. Conviction Narrative Updating
CNT proposes that decisions made under radical uncertainty require actors
to develop narratives that plot the outcome of their proposed actions so that it
gives them sufficient conviction to act. At the time their decisions are made, it is
not possible to know whether the conviction they have is overconfident – the
outcome of the kind of misguided optimism or bold forecasting that authors like
Kahneman and Lovallo (1993) have been concerned about. In radical uncertainty,
we simply cannot know.
Although there is no way to know whether a chosen decision is optimal by
applying the probability calculus, a different way to approach the underlying
problem is suggested by CNT.
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Radical uncertainty necessarily evokes the possibility that actions can
succeed or fail. If decision-making is to be resilient, one question is whether
conviction is generated after adequately considering failure as well as success and
also whether or not new information that comes in is adequately evaluated.
Tuckett (2011; Tuckett and Taffler, 2008) proposed the concepts of
Integrated (IS) and Divided State (DS) mental states. They are dispositional
properties (Stinchcombe, 2005) that refer to relations between thoughts and the
feelings they evoke.
One state, DS, is conceived as an orientation towards a particular narrative
characterised by the apparent absence of felt ambivalence - (Smelser, 1998) - the
simultaneous existence of contradictory feelings of approach and avoidance. It is
recognisable because although different outcomes are conceivable, conflicting
narratives are absent. A conflicting narrative for this purpose is one in which the
potential outcomes of action evoke the opposite (approach or avoidance) emotional
state to that the subject is now in. In DS feelings like doubt, frustration,
humiliation, defeat or disappointment, for example, which might evoke avoidance
and create a shift towards abandonment of the current exciting, promising,
fulfilling, narrative, are absent. In DS only partial non-ambivalent narratives of
self and other relationships are allowed.
An Integrated State (IS) is an alternative mental disposition. It is
characterised by the emotional ability to tolerate feelings of doubt or ambivalence
when they are aroused by thoughts and to retain curiosity about both their source
and potential evolution. In such states, actors can reflect on alternative and
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26
contradictory narratives of the future and act even if some thoughts create
unpleasant feelings because they know they threaten the outcome of plans.
IS and DS are conceived as omnipresent and shifting states, each with very
different implications for appraising the outcome of action. They influence
perception of elements in an actor’s environment and are influenced by shifts
within it – other people’s behaviour, news, innovation, etc. They exist as
dispositions simultaneously and overlap one another but with one always
dominating mental proceedings at any one moment
6
. This is in sharp
contradistinction to modelling in a range of theories (such as neural network,
consistency theories and connectionist theories) which assume immediate
resolution.
The point is that updating a narrative in the face of new information works
differentially in DS and IS, as
represented in Figure 3. In IS new
information evoking approach and
avoidance emotions is registered, even if
it threatens the current narrative.
Therefore, when new information is
available for appraisal it encounters no
special barrier. In IS ambivalence and
the fact and relevance of deep
6
A possible way to conceive their relationship might be in terms of Busemeyer and Bruza’s (2011) discussion of how quantum theory can
be applied to decision-making. They illustrate the idea describing the potential trajectory of a jury member’s thinking about whether a
defendant is innocent or guilty as different bits of evidence are unfurled.)
Figure 3. The Influence of DS and IS States of Mind on the
Likelihood New Information Felt Incongruent Will Shift a
Preferred Narrative
June 10 version
27
uncertainty are accepted. Action can be altered and information evoking approach
and avoidance emotion can be processed without inducing panic or paralysis.
By contrast, in DS new information elements which are congruent with the
existing narrative reinforce conviction but non-congruent information elements
are blocked. They still exist “somewhere” as a dispositional element ‘behind the
scenes’, ready to be invoked but unavailable for reflective thought.
From a CNT perspective we hypothesise that both the disappearance of
thematic diversity and sustained directional shifts in the relative proportion of
approach and avoidance emotion can be used as indicators to assess how far Ds is
dominating a decision-making environment. An exploratory study using
algorithmic measures on a large database of texts conducted in collaboration with
the Bank of England suggests it is possible to measure changes in thematic
diversity. The study demonstrated that there was less thematic diversity, to a
statistically significant degree, in news databases prior to the financial crisis
(Nyman et al, 2016; Nyman, 2015). In this period markets seem to have been
captured by an homogenisation of narrative content. A second study conducted in
parallel, measured shifts in the proportion of approach and avoidance words in
news narratives around particularly relevant topics in the period 1996-2013:
liquidity (relevant to discussion of default risk in financial markets) and Fannie
Mae (relevant to narratives about US mortgage backed assets). Both studies
showed that in the period leading into the crisis there were statistically unusual
sentiment shifts in which avoidance emotions increasingly disappeared – even
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28
after there was news suggesting the American housing market was in trouble
(Tuckett et al, 2014; Tuckett, 2014).
9. Conclusion.
In this paper, we have set out a general framework to try to broaden
decision-making theory to enable it better to understand and analyse how
subjectively means-end rational actors cope so that they can make decisions when
the assumptions of traditional optimising models fail to hold.
The framework focuses on the power of narrative and emotion to combine
to predict outcomes that facilitate action in the situation in which the decision-
maker is located.
We have referred to the long tradition in social science (including in
psychology) that emphasises the importance of factors such as beliefs, rhetoric,
models and accounts in precipitating action. We have mentioned the substantial
tradition in psychology, largely deriving from Herbert Simon, which has both
questioned optimisation models and focused on the role of satisficing, adaptive
heuristics or pattern recognition. Figure 2 has set out how some of these well-
recognised factors combine to support action through evoking approach feelings.
The central claim in CNT (Figure 1), which differentiates it from the
standard approach focused on optimising subjective utility, is that feelings
(specifically the dominance of approach over avoidance emotion made possible by
the preferred narrative linking action to desired outcomes) play a necessary but
not sufficient role in decisions to act under radical uncertainty, however these
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decisions turn out. All the elements in the narrative – the more or less explicit
models, beliefs, decision rules, methods for repelling doubt, etc., underlying the
story linking action to outcome - are evaluated emotionally as well as cognitively
in the local context (Figure 2). Ultimately, the imagined outcome of the action and
the chosen tools for bringing it about evoke approach rather than avoidance and
therefore, feels accurately estimated, based on the models and beliefs they rest on.
We suggest that without the emotional component, that is without the human
capacity to create “felt” knowledge and so to develop conviction in narratives,
action in radical uncertainty would in a sense be subjectively irrational. Actors
trust their outcome narratives, decision rules, sources and sections of information,
and causal models, because they are what they have arrived at through their lives,
and as they evaluate them cognitively they experience them as accurate.
Kahneman (2011) set out the core premises on which the dominant
heuristics and biases approach to decision making was built. In essence, he and
Tversky observed faulty decision-making that they judged to be over-reliant on
feelings, intuitive predictions, local heuristics, biased framing, etc. In contexts in
which data is available to select an optimal action such insights are important.
But in radical uncertainty things are less clear. For instance, according to
Kahneman & Lovallo (1993), decision makers have a “strong tendency to consider
problems as unique” (p17) and tend to suffer from an optimism bias. Thinking
about decisions such as to merge their firm with another they “routinely
exaggerate the benefits and discount the costs, setting themselves up for failure”
(Lovallo and Kahneman, 2003, p57). If the outcome of mergers can be represented
June 10 version
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as a series of independent decisions drawn from a common pool then clearly you
should look beyond your own situation and apply probabilistic reasoning in the
usual way. However, a data search will reveal famous examples of mergers that
work and that do not, clearly or partially (e.g. Tichy, 2001). Reflection is clearly
desirable but optimising from a “full” dataset may or may not be inferior to a
simple heuristic (Gigerenzer, 2014). The problem is not simply one of an inside or
outside view but one of subjective judgment as to what outcomes are likely when
you cannot know – not so much how to analyse data but how to know what data is
relevant. While systematic analysis can expand a debate to compare the current
decisions with past decisions, there will be many open questions when deciding
how far the current merger proposition does or does not share characteristics with
past successes or failures. Under radical uncertainty, there can be no definitive
answer, only plenty of room for debate.
CNT should not be understood to extol the value of the inside versus the
outside view. Rather, it aims to add to the standard perspective by identifying the
specific problems of judgment and interpretation that actors must cope with when
trying to become convinced in any context in which they have large quantities of
data relevant to the outcomes of action but radical uncertainty as to its
implications. CNT applies, therefore, to those situations in which there is doubt
as to what data to select and which rules of thumb, models and beliefs to rely on.
Because the judgmental challenge in radical uncertainty is not the same as in risk,
CNT prioritises the need to understand and research not whether but how people
select particular beliefs, narratives, tools and data to create a feeling of conviction.
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Returning to situations like organisational mergers, the Ds concept (and the
potential to measure it in several ways) suggests a mode of analysis with the
potential to make predictions based on ex ante measures of content diversity and
emotional shifts. Radical uncertainty means that it is impossible without
hindsight to know the best course. But the regularities we hypothesise exist in the
way human decision-makers cope with uncertainty (either in Ds or IS) would not
be subject to this limitation. Decision-making capture by a preponderance of Ds is
unlikely to prove resilient.
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We would like to thank Claudia Ruatti for working on an extensive earlier draft on the topic and Liz Allison, Aikaterini
Fotopoulou, Konstantinos Katsikopoulos and David Vinson for several readings and extensive comments. Peter Ayton,
Christos Bechlivanidis, Vittorio Gallese, Andreas Gloekner, David Good, Adam Harris, Nigel Harvey, David Lagnado,
Franziska Leutner, Brad Love, Mark Fenton O’Creevy, Magda Osman, Paul Ormerod, David Shanks, Robert Elliott
Smith, Dennis Snower, George Soros and Victor Emmanuel Strauss made helpful comments on these ideas or on earlier
versions of this manuscript. This work and the Centre for the Study of Decision-Making Uncertainty has been generously
supported by the Institute of New Economic Thinking (grants no. IN01100025 and IN1300051) and by the Eric
Simenhauer Fund of the British Psychoanalytic Society.
