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The Effectiveness of Simple Decision Heuristics: Forecasting Commercial Success for Early-Stage Ventures

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Abstract

We investigate the decision heuristics used by experts to forecast that early-stage ventures are subsequently commercialized. Experts evaluate 37 project characteristics and subjectively combine data on all cues by examining both critical flaws and positive factors to arrive at a forecast. A conjunctive model is used to describe their process, which sums "good" and "bad" cue counts separately. This model achieves a 91.8% forecasting accuracy of the experts' correct forecasts. The model correctly predicts 86.0% of outcomes in out-of-sample, out-of-time tests. Results indicate that reasonably simple decision heuristics can perform well in a natural and very difficult decision-making context.

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... Other studies thus question the validity and reliability of expert judgments, and rather suggest using models or deterministic rules to pick winners (see e.g. Meehl, 1954;Shanteau and Stewart, 1992;Kahneman and Klein, 2009;Åstebro and Elhedhli, 2006). Hence, learning about effective 62 In this paper, we consider growth-oriented firms as gazelles, i.e. firms that have very high marginal return to capital and show potential or actual fast growth in turnover or employment. ...
... 70 Defining targeting criteria may be relatively easy given the general objective of the BPC. Yet, Freel (1998) argues such criteria can be arbitrary because they do not predict well the performance of 'winner entrepreneurs' or the number of criteria employed may affect the accuracy of experts' predictions (see also Stewart 1988;Besley and Kanbur, 1991;Zacharakis and Meyer, 2000;Åstebro and Elhedhli, 2006). In particular, Robalino et al. (2020) show that when the objective is to maximize jobs impacts of investment projects, a key selection criterion should be the evaluated jobs-linked externalities of each project. ...
... Moreover, the agency must ensure that the general ranking of entrepreneurs does not change considerably if new experts are considered or the business plans are reshuffled among the individual experts (Payne et al., 1993;Zacharakis and Meyer, 2000). If reshuffling the plans changes the ranking considerably (Åstebro and Elhedhli, 2006), then it suggests that the winning entrepreneurs will be growth-oriented only by chance (see Goldberg, 1968;Wren, 2003). ...
Thesis
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Most Sub-Saharan African (SSA) countries experienced sound economic growth and a declining rate of poverty over the last two decades. Though, by far, the SSA region remains the poorest in the world and faces tremendous political, social, and economic challenges. Moreover, due to the COVID-19 pandemic, SSA entered into a recession with a GDP growth rate of minus 5% in 2020 as ever recorded over 25 years. This has also induced an increase in poverty in the region, which adds up to the structural challenges and further highlight the need of sound policies to address economic growth, governance, jobs, and poverty for the region to meet the Sustainable Development Goals (SDGs) in 2030 and beyond. This thesis examines the effects of institutional quality, political instability, and a government targeted entrepreneurship program on the accumulation of human, physical, and financial capital by households and firms. In the literature, these factors are identified as the key determinants of economic growth and job creation, yet this thesis contributes to a knowledge gap, especially at the microeconomic level, on how households and firms accumulate these factors in the presence of weak institutional quality, political instability, and government targeted entrepreneurship programs. In particular, this thesis investigates heterogeneity as well as a single country study of the effects of institutional quality and political instability; it also employs a randomized controlled trial (RCT) to assess the impacts of two different targeted entrepreneurship support programs; and finally, it taps on data from this field experiment to assess the performance of two different targeting mechanisms for selecting growth-oriented entrepreneurs. Each paper is self-contained and three among the four papers were written with co-authors. The first paper assesses the effects of institutional quality and political instability on household assets and human capital accumulation in 19 Sub-Saharan African countries for the period 2003-16. In this paper, the concept of instability is enlarged to include factual instability as measured by the number of political violence and civil unrest events, perceived instability as measured by the perceptions of the quality of institutions by households, and the interplay between factual and perceived instability. Contrary to most previous analyses, this paper takes into account household wealth distribution to show how the effects of political instability differ for poor vs. rich households. For identification, I exploit the variation of factual and perceived instability across 185 administrative regions in the 19 countries. My regressions control for a large range of confounding factors measured at the levels of households, regions, and countries. Overall, factual and perceived instability are associated with higher investments in assets, and factual instability is also associated with more investment in house improvements, yet it is negatively associated with the ownership of financial accounts. With regard to the heterogeneous effects, increased factual or perceived instability is associated with more investments in physical capital but less investments in financial and human capital among rich households, and with less investments in physical, financial and human capital among poor households. These findings suggest that political instability might enhance the accumulation of wealth by rich households and reduce that of poor households, implying that the detrimental effects of political instability have lasting consequences for poor households, especially when poor households are exposed to an actual or even just perceived deteriorating quality of the country’s institutions. The second paper, written with Nicolas Büttner and Michael Grimm, analyzes households’ investments in assets and their consumption, and education and health expenditures when exposed to actual instability as measured by the number of political violence and protest events in Burkina Faso. There is a large, rather macroeconomic, literature that shows that political instability and social conflict are associated with poor economic outcomes including lower investment and reduced economic growth. However, there is only very little research on the impact of instability on households’ behavior, in particular their saving and investment decisions. This paper merges six rounds of household survey data and a geo-referenced time series of politically motivated events and fatalities from the Armed Conflict Location and Event Data project (ACLED) to analyze households’ decisions when exposed to instability in Burkina Faso. For identification, the paper exploits variation in the intensity of political instability across time and space while controlling for time-effects and municipality fixed effects as well as rainfall and nighttime light intensity, and many other potential confounders. The results show a negative effect of political instability on financial savings, the accumulation of durables, investment in house improvements, as well as on investment in education and health. Instability seems, in particular, to lead to a reshuffling from investment expenditures to increased food consumption, implying lower growth prospects in the future. With respect to economic growth, the sizable education and health effects seem to be particularly worrisome. The third paper, written with Michael Grimm and Michael Weber, employs a randomized controlled trial (RCT) to assess the short-term effects of a government support program targeted at already existing and new firms located in a semi-urban area in Burkina Faso. Most support programs targeted at small firms in low- and middle-income countries fail to generate transformative effects and employment at a larger scale. Bad targeting, too little flexibility and the limited size of the support are some of the factors that are often seen as important constraints. This paper assesses the short-term effects of a randomized targeted government support program to a pool of small and medium-sized firms that have been selected based on a rigorous business plan competition (BPC). One group received large cash grants of up to US$8,000, flexible in use. A second group received cash grants of an equally important size, but earmarked to business development services (BDSs) and thus less flexible and with a required own contribution of 20%. A third group serves as a control group. All firms operate in agri-business or related activities in a semi-urban area in the Centre-Est and Centre-Sud regions of Burkina Faso. An assessment of the short-term impacts shows that beneficiaries of cash grants engage in better business practices, such as formalization and bookkeeping. They also invest more, though, this does not translate into higher profits and employment yet. Beneficiaries of cash grants and BDSs show a higher ability to innovate. The results also show that cash grants cushioned the adverse effects of the COVID-19 pandemic for the beneficiaries. More generally, this study adds to the thin literature on support programs implemented in a fragile-state context. The fourth paper, written with Michael Weber, examines the selection of entrepreneurs based on expert judgments for a BPC in Burkina Faso. To support job creation in developing countries, governments allocate significant funds to a typically small number of new or already existing micro, small, and medium-sized enterprises (MSMEs) that are growth oriented. Increasingly, these enterprises are picked through BPCs where thematic experts are asked to make the selection. So far, there exists contrasting and limited evidence on the effectiveness and efficiency of these expert judgments for screening growth-oriented entrepreneurs among contestants in BPCs. Alternative or complementary approaches such as evaluation and selection algorithms are discussed in the literature but evidence on their performance is thin. This paper uses a principal component analysis (PCA) to build a metric for comparing the performance of these alternative mechanisms for targeting entrepreneurs with high potential to grow. The results show expert subjectivity bias in judging contestant entrepreneurs. The paper finds that the scores from the expert judgment and those from the algorithm perform similarly well for picking the top-ranked or talented entrepreneurs. It also finds that both types of scores have predictive power, i.e. have statistically significantly associated with 17 firm performance outcomes measured 10 or 34 months after the BPC started. Yet, the predictive power, as measured by the magnitude of the regression coefficients, is higher for the algorithm metric, even when it is considered jointly with expert judgment scores. Despite the statistical superiority of the algorithm, expert assessments at least through pitches of entrepreneurs have proved useful in many settings where free-riding or misuse of public funds may occur. Hence, efficiency and precision could be achieved by relying on a reasoned combination of expert judgments and an algorithm for targeting growth-oriented entrepreneurs. These four papers bring new insights on the relationship between weak institutions, political instability, and targeted government support to entrepreneurship for increasing the accumulation of financial, physical, and human capital, and productivity. And these are the key factors for spurring economic growth and creating jobs in SSA. These findings suggest that efficient institutions building in SSA countries would enhance citizen perceptions of good governance which would reduce political instability and enable households including the poor to accumulate productive assets, increase their productivity and reduce poverty. The findings also suggest that targeted government entrepreneurship support programs, e.g. in the forms of cash grants with monitored disbursements yet flexible in use, can enhance firms’ human capital, productive assets, and innovations, even in the short term. Moreover, the targeting mechanism of such programs could be made more effective and efficient by relying on a combinaison of expert judgments and an algorithm for picking growth-oriented entrepreneurs.
... For instance, when deciding which university is better than another, the decision maker tries to collect all information on the universities and combines (weights) the available information into a single judgement. The opposite of this "rational " approach is the heuristics approach, which relies on just one dimension (or only a few dimensions) in order to make decisions (see, e.g., Astebro & Elhedhli, 2006 ). Heuristics process the information using relatively simple computations (e.g., based on one good reason), and are easy to explain and understand. ...
... Models that are ecologically rational can be separated from unbounded rationality models: these "models provide unrealistic descriptions of how people make decisions, given our limited time, knowledge, and computational power " ( Marewski, Schooler, & Gigerenzer, 2010 , p. 73). Ecologically rational models can be called 'satisfying' decision strategies (following Simon, 1955Simon, , 1956 ) "combining two goals: that of satisfying a pre-defined level of accuracy and that of using sufficient mental computational processing for a boundedly rational decision-maker " ( Astebro & Elhedhli, 2006 ). Furthermore, these models might be 'successful' because they focus on a few, valid cues and try to ignore 'noise' (see Scheibehenne & von Helversen, 2009 ). ...
... As outlined by Bornmann and Marewski (2019) , various methods have been proposed and used for studying heuristics in areas other than research evaluation. For example, Astebro and Elhedhli (2006) used interviews to explore the use of heuristics. ...
Article
Bornmann and Marewski (2019) have adapted the concept of fast-and-frugal heuristics to scientometrics in order to study and guide the application of bibliometrics in research evaluation. Bibliometrics-based heuristics (BBHs) are simple decision strategies for evaluative purposes based on bibliometric indicators. One aim of the heuristics research program is to develop methods for studying the use of BBHs in research evaluation. Many deans probably evaluate rough performance differences between researchers in their departments based on h index values. Bornmann, Ganser, Tekles, and Leydesdorff (2020) developed the Stata command h_index and R package hindex which can be deployed in a fast and frugal way to decide on the following question: can the h index be used to compare all researchers in a university department, or are the citation cultures so different between sub-groups in the department that not all researchers can be compared with one another? The command and package can be used for simulations that might answer the question before extensive processes of data collection start. If the citation cultures are very different in the sub-groups, the researchers should be compared with field-normalized indicators (instead of the h index). This paper shows how the h_index command and hindex package can be employed for the decision on the h index use in the BBH.
... The tool, called the Critical Factor Assessment, uses a 37-factor predictive model that was developed by the Canadian Innovation Centre and shown to have strong predictive value in forecasting commercial success or failure by Astebro and Elhedhli. [5] We adapted a reduced, eight-factor version of the Critical Factor Assessment named the "CFA Snapshot" that is publicly available on the Canadian Innovation Centre website (www.innovationcentre.ca). It includes self-assessment criteria that prospective entrepeneurs can use to gauge the commercial viability of their proposed ventures. ...
... Nevertheless, the program's essential elements are theoretically and empirically grounded; fill an important gap at research universities; were identified in response to what worked and did not work in a previous award program; and apply an approach to forecasting the success of entrepreneurial ventures that has demonstrated predictive validity. [5] We see the foundational elements of the program as these: ...
Article
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Background/Objective Although most research universities offer investigators help in obtaining patents for inventions, investigators generally have few resources for scaling up non-patentable innovations, such as health behavior change interventions. In 2017, the dissemination and implementation (D & I) team at the University of Wisconsin’s Clinical and Translational Science Award (CTSA) created the Evidence-to-Implementation (E2I) award to encourage the scale-up of proven, non-patentable health interventions. The award was intended to give investigators financial support and business expertise to prepare evidence-based interventions for scale-up. Methods The D & I team adapted a set of criteria named Critical Factors Assessment, which has proven effective in predicting the success of entrepreneurial ventures outside the health care environment, to use as review criteria for the program. In March 2018 and February 2020, multidisciplinary panels assessed proposals using a review process loosely based on the one used by the NIH for grant proposals, replacing the traditional NIH scoring criteria with the eight predictive factors included in Critical Factors Assessment. Results two applications in 2018 and three applications in 2020 earned awards. Funding has ended for the first two awardees, and both innovations have advanced successfully. Conclusion Late-stage translation, though often overlooked by the academic community, is essential to maximizing the overall impact of the science generated by CTSAs. The Evidence-to-implementation award provides a working model for supporting late-stage translation within a CTSA environment.
... Heuristics consist of ordered cues that offer a means to minimize noise and, thereby, often outperform the cognitive advantages of logic and probability (Åstebro & Elhedhli, 2006). By using heuristics, decision-makers can "forget" data and focus on pertinent issues. ...
... Managers have been found to make decisions more quickly (Day & Lord, 1992;Fredrickson, 1985;Isenberg, 1986); to be unaffected by context (Fredrickson, 1985) and to require less information (Isenberg, 1986). Managers have also reported outperforming statistical forecasting models in predicting the likelihood that an invention reaches the market (Åstebro & Elhedhli, 2006). They have also been found to keep their heuristics portfolio small and prune the number of heuristics used as they gain experience (Bingham & Eisenhardt, 2011). ...
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The initial screening decision that marketing managers make is critical. It requires the selection of what innovation project to invest in, which is fundamental to marketing success. However, our knowledge of how managers make these decisions and how this impacts performance is limited. By drawing upon cognitive psychology and the managerial decision‐making literature, we address two critical questions. The first question focuses on identifying specific decision‐making types (e.g., specific heuristics, intuition) used when making an innovation screening decision. Based on this analysis and prior research, we develop specific decision‐maker profiles about how an individual manager decides. The second research question is about connecting these profiles with performance. Specifically, it addresses what the consequences of different decision‐maker profiles are on the perceived accuracy and speed of decision‐making? Data were collected from 122 senior managers in these industries. We find that when heuristics are used alone, or concurrently with intuition, managers make decisions that are as accurate as when they rely on analytical decision‐making. However, the process is significantly faster. The findings provide an important step toward a more comprehensive understanding of decision‐making at the front‐end of innovation.
... Our experiments confirm some effects hypothesized in previous research while going further by presenting new insights into decisionmaking regarding both decomposable and non-decomposable decision tasks. Confirming manipulated expertize effects reported in the literature (Åstebro & Elhedhli, 2006;Dane et al., 2012;Wübben & Wangenheim, 2008), our results show that experts outperform nonexperts in decomposable decision tasks. However, this advantage disappears when experts undertake a non-decomposable decision task. ...
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Marketing experts are tasked with making important decisions that influence firms' performance. Some decision tasks are decomposable and can be broken down into smaller parts (e.g., pricing new products). Others are non‐decomposable and are challenging to break down (e.g., selecting creative work for advertising campaigns). The literature remains divided on whether expertise aids decision‐makers in addressing these different decision tasks, as well as how different decision‐making processes (critical analysis, intuition, introspection) improve decision‐makers' performance when they face these tasks. Using experiments with comparative samples of senior marketing managers (experts) and general public participants (non‐experts), we test whether expertise provides advantages when making decisions. Our results suggest that experts perform better than the general public with decomposable decision tasks, though not with non‐decomposable decision tasks. Furthermore, decision‐makers who rely on critical analysis perform better compared to intuition when addressing decomposable decision tasks, but the decision process is less important with non‐decomposable decision tasks. These findings provide insight into the conceptual boundaries of marketing professionals' expertise. Managers could apply these insights to potentially save resources (e.g., time, finances) by delegating decisions to more junior staff or even by leveraging external counsel through crowdsourcing.
... The chronological approach highlights the evolution of opinions or trends over time, while the thematic approach examines the popularity of theories and their evolution over time and the methodological approach highlights the main econometric and statistical models and techniques used to process the data. Our approach to this systematic review is consistent with extant studies on surveys and literature reviews (Asongu, 2016;Asongu et al., 2017;. ...
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In developing countries, taxation is perceived as a brake on economic growth. Indeed, taxes in most of these countries are not sufficiently adapted to the specificity of the taxpayer and often do not consider the weak administrative capacity of the countries in the region. In this context, reforms have been initiated over the last decade to create tax environments that encourage savings, investment, entrepreneurship, and social innovation. This study provides an overview of research on the effects of taxation on social innovation and the corresponding implications for the achievement of Sustainable Development Goals (SDGs) in developing countries, taking three approaches: thematic, chronological, and methodological. Most studies agree that high taxes in business undermine social innovation and thus the achievement of SDGs, as social innovation is known to be a driver of most SDGs and business the vehicle. The majority of the selected studies used primary data collected from samples whose representativeness with respect to the population concerned (notably businesses) is still not explicitly justified.
... Starting from the debate on heuristics in psychology and cognitive science, the marketing and management literature has been in many ways permeable to the influences of such debate (Loock and Hinnen, 2015) in many directions: marketing and management scholars used not only heuristics (Wübben and Wangenheim, 2008;Bingham and Eisenhardt, 2011) but also applied psychology and cognitive sciences researchers' publishings on marketing and management journals (Katsikopoulos and Gigerenzer, 2013;Brighton and Gigerenzer, 2015). Many of these studies reproduce the comparison between an approach to heuristics as a source of error (Manimala, 1992;Busenitz and Barney, 1997) and an approach to heuristics as rules of thumb that lead to effective solutions to complicated problems (Eisenhardt and Sull, 2001;Åstebro and Elhedhli, 2006;Katsikopoulos and Gigerenzer, 2013;Abatecola et al., 2018;Cristofaro and Giannetti, 2021). ...
Article
Purpose This paper aims to provide a wide picture of studies on heuristics for international decision-making with a focus on foreign market entry. This paper systematically reviews studies published in the international business and international marketing domain to examine heuristically based decisions for foreign market entry. Design/methodology/approach This paper proposes a systematic literature review and an in-depth analysis of 32 papers published between 1997 and 2021 dealing with foreign market entry and the use of heuristics for international decision-making. Findings Even if the marketing and management literature is in many ways permeable to the debate around heuristics developed in experimental psychology and cognitive science, international business and international marketing studies on the one hand recognize that international decision-making, especially when dealing with foreign market entry, is strongly characterized by uncertainty, on the other hand, there isn’t a developed and systematized literature about it. This paper shows key topics and areas fundamental to foreign market entry in which heuristics are applied by decision makers and their effectiveness. Originality/value A systematic review of the use of heuristics for foreign market entry decision-making can represent a useful step for a more organic development of knowledge about the more general use of heuristics for international decision-making. Understanding the decision-making process on the modes of entry in foreign markets is a key topic for international marketing and international business scholars and practitioners.
... A burgeoning management literature concludes that managers often employ heuristics (Bingham & Eisenhardt, 2011) and that they can be effective decision-making tools in the face of uncertainty (Artinger et al., 2015). For example, Å stebro and Elhedhli (2006) show that simple heuristics can be more effective than complex regression models in predicting the success of risky ventures. That is, even in complex real world managerial domains, heuristics are not necessarily a second-best solution, particularly in large world environments were uncertainty and noise reign supreme. ...
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We study the performance of heuristics relative to the performance of optimal solutions in the rich domain of sequential search, where the decision to stop the search depends only on the applicant’s relative rank. Considering multiple variants of the secretary problem, that vary from one another in their formulation and method of solution, we find that descriptive heuristics perform well only when the optimal solution prescribes a single threshold value. We show that a computational heuristic originally proposed as an approximate solution to a single variant of the secretary problem performs equally well in many other variants where the optimal solution prescribes multiple threshold values that gradually relax the criterion for stopping the search. Finally, we propose a new heuristic with near optimal performance in a competitive or strategic variant of the secretary problem with multiple employers competing with one another to hire job applicants. Both heuristics share a simple computational component: the ratio of the number of interviewed applicants to the number of those remaining to be searched. We present the subgame-perfect Nash equilibrium for this competitive variant and an algorithm for its computation.
... (respondent 11) Managers learn heuristics from experience (Bingham and Haleblian, 2012) and employ them purposefully (Basel and Brühl, 2013) to guide their decisions in complex situations. Learned heuristics have been positively associated with effective decision-making (Åstebro and Elhedhli, 2006;Gigerenzer, 2008;Wübben and v. Wangenheim, 2008), but also with many aspects of management beyond decision-making, from strategy (Bingham and Eisenhardt, 2011;Eisenhardt and Sull, 2001) and innovation (Manimala, 1992) to organizational learning (Bingham and Haleblian, 2012) and the firm's survival All three streams have a common understanding of heuristics as 'cognitive shortcuts that emerge when information, time, and processing capacity are limited' (Newell and Simon, 1972, definition cited in papers from all streams: Abatecola, 2014;Bingham and Eisenhardt, 2011;Guercini et al., 2015;Shah and Oppenheimer, 2008;Vuori and Vuori, 2014) and they all stress the important role of heuristics in human and managerial behaviour. The heuristics-and-biases approach and the fast-and-frugal approach share their focus on the immediate outcome of heuristics as decision-making tools, but differ in their conclusion (a negative view versus a positive view) and in the way the outcome (the accuracy of the decision or the prediction) should be assessed. ...
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Managerial heuristics play an important role in decision‐making and positively contribute to strategy, innovation, organizational learning, and even the survival of a firm. Little is known, though, about the process through which heuristics emerge. Following a grounded theory approach, we develop a process model of how managers create and develop heuristics from experience. The 4‐step model ‐ dissonancing, realizing, crystallizing, and organizing ‐ captures the sequence of cognitive schemata that start with a flawed assumption, give rise to heuristics that tend to be born in pairs, and end with mature and shared heuristics. With these findings, we contribute to the literature on heuristics by offering a model for the process of their emergence, a view on how feelings initiate, guide, and strengthen this process, and a description of the role played by the environment, enriching the ecological rationality perspective.
... In other words, it is essential to identify the market status before coming up with marketing plans. This research provides an effective CVS method that enables marketers formulate marketing plans based on identified market status, key indicators of customer value and the product category of their business, with less workloads in collecting and processing data, but higher prediction efficiency (Thomas and Samir, 2006). To specify, marketer could apply the proposed model to calculate the customer value ranking scores of the products/brands/companies, thus identifying the potential market value growth by competitors and predicting their value-creation stage and trend in future. ...
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Purpose This study aims to introduce a compelling customer value score method (CVSM), which is applicable for different product categories, and elaborates customer values in three components (direct economic value, depth of direct economic value and breadth of the indirect economic value) throughout three stages of customer journey. Design/methodology/approach This study collected data from the Internet-shopping platforms, namely Taobao and T-Mall from 2019 to 2020 with particular focus on three product categories: lipstick (fast-moving consumer goods), mobile phones (durable goods) and alcohol (a hybrid of the other two product types) from 37 selected firms. The CVSM employs an entropy-based multiple criteria analysis, of which the weight of each indictor is not fixed artificially, but computed by the entropy-based method that calculates informative differences among the indicators (profit, revenue, positive reviews, search index and likes and favorites). Findings The result shows that product categories and market status have a moderation effect on three components in customer values. The findings suggested marketing strategies for different consumer goods, where the fast-moving consumer goods like lipstick should focus on the pre-purchase stage while the durable goods should emphasize post-purchase stage when the market is rapidly changing. Originality/value The study brings new insights to Kumar’s customer value theory by integrating product categories and the market status, revealing that three components of customer values differ in their contributions to the whole customer values. This paper further contributed managerial suggestions for marketers with regards to three stages of customer journey.
... Beyond this, prior work supports the contention that scientists use a variety of heuristics to judge whether or not ideas are worth pursuing (Haller and Welch, 2014). While it is true that the heuristics scientists use may be biased, they are also informed by diverse practices and experiences in ways that are often difficult to measure (Astebro and Elhedhli, 2006;Shane et al., 2015). As a result, some academic inventors may be more willing to engage in further development of a patent if they judge it to have commercial potential. ...
Article
This study expands the scope of research on academic entrepreneurship to include academic inventors who actively engage in late-stage commercialization. It investigates post-patent involvement of academic scientists in the development of products based on their patented inventions. Using data from a 2010 national survey of 798 academic inventors listed on patents assigned to universities in 2006, our analysis shows that only 27% of the inventors were working with a company to further develop their invention for commercial use. Additionally, academic inventors who reported stronger entrepreneurial orientation, higher commercial significance of the patent, lower reliance of the patent on scientific literature, and stronger entrepreneurial disposition of their university were more likely to engage in post-patent commercial development. Our work contributes to the literature on the entrepreneurial behavior of academic scientists by further exploring a critical but relatively understudied post-invention stage of commercialization.
... In contrast, prospective evaluation, such as in grant-proposal review, occurs in a very different information environment, in which the true underlying strengths and weaknesses of the work are not fully known to either authors or evaluators. Such evaluation is largely about forecasting the future, and a focus on the negatives (compared with a more balanced approach) may be suboptimal (Åstebro and Elhedhli 2006). Yet, evaluators may, nevertheless, bring the typical focus on the negatives to prospective evaluations as well (Gallo et al. 2016). ...
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The evaluation and selection of novel projects lies at the heart of scientific and technological innovation, and yet there are persistent concerns about bias, such as conservatism. This paper investigates the role that the format of evaluation, specifically information sharing among expert evaluators, plays in generating conservative decisions. We executed two field experiments in two separate grant-funding opportunities at a leading research university, mobilizing 369 evaluators from seven universities to evaluate 97 projects, resulting in 761 proposal-evaluation pairs and more than $250,000 in awards. We exogenously varied the relative valence (positive and negative) of others’ scores and measured how exposures to higher and lower scores affect the focal evaluator’s propensity to change their initial score. We found causal evidence of a negativity bias, where evaluators lower their scores by more points after seeing scores more critical than their own rather than raise them after seeing more favorable scores. Qualitative coding of the evaluators’ justifications for score changes reveals that exposures to lower scores were associated with greater attention to uncovering weaknesses, whereas exposures to neutral or higher scores were associated with increased emphasis on nonevaluation criteria, such as confidence in one’s judgment. The greater power of negative information suggests that information sharing among expert evaluators can lead to more conservative allocation decisions that favor protecting against failure rather than maximizing success. This paper was accepted by Alfonso Gambardella, business strategy.
... To determine the extent of belief updating, the entrepreneur may refer to similarity based heuristics (Artinger et al., 2015), such as updating towards imitating analogical successful offerings ("take the option that you recognize") ; satisfice ("update until the product is good enough") (Grandori, 2010;Savage, 1954); update taking into account the affordable loss ("don't throw good money after bad money") (Dew et al., 2009); or combine such heuristics (Åstebro & Elhedhli, 2006). These heuristics provide a basis for the decision of which way to go in responding by providing a rule when to stop searching for a (presumably) better solution. ...
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Building on pragmatism, we advance an entrepreneur-as-scientist perspective and depict entrepreneurs as engaging in causally inferential action by forming beliefs, testing these beliefs, and responding to the feedback received. However, this sequence of entrepreneurial actions arrives with a set of companion doubts, namely doubt about product-market fit because the entrepreneurs’ beliefs are self-chosen, doubt about feedback validity from false positives or false negatives, and doubt about over- and underfitting in responses to feedback. We discuss the rationality of heuristics deployed by the entrepreneur to overcome these doubts. Our insights contribute to the micro-foundations of entrepreneurial action and strategy by explaining how entrepreneurs generate the information to produce value out of uncertainty.
... This logic implies that voters did not vote for Trump; they instead voted against the previous governing party and its candidate. Åstebro and Elhedhli (2006) report evidence that a tallying heuristic for classifying early stage ventures performs at least as well as computationally intensive models, while being faster and requiring less information. The heuristic first tallies the positive and negative attributes of a specific venture. ...
Thesis
Diese Dissertation unterstreicht die Rolle von Informationen bei der Erforschung ökonomischer Entscheidungen. Das erste Kapitel beschäftigt sich mit dem Zugang zu akkuraten Informationen über die Wirksamkeit medizinischer Interventionen. Unserem Model zufolge besteht in vielen entwickelten Ländern Nachfrage für Zugang zu unabhängigen medizinischen Informationen wie Cochrane Reviews. Wir schätzen, dass für viele Länder diese Nachfrage zu moderaten oder geringen Kosten erfüllt werden kann. Das zweite Kapitel beschäftigt sich mit der Kommunikation solcher Informationen und untersucht den unterstützenden Effekt natürlicher Häufigkeiten bei der Berechnung von A-posteriori Wahrscheinlichkeiten (Gigerenzer & Hoffrage, 1995). Durch eine Meta-Analyse erklären wir Konzepte und entflechten die Effekte von 15 Studienmerkmalen. Im einfachsten Studiendesign führen natürliche Häufigkeiten zu 24 Prozent korrekten Antworten verglichen mit 4 Prozent bei konditionellen Wahrscheinlichkeiten. Die finalen beiden Kapitel analysieren Satisficing-Strategien für unsichere Entscheidungsumgebungen in denen Agenten eine vollumfassende, probabilistische Beschreibung des Entscheidungsproblems fehlt. Simon (1955) zufolge nutzen Satisficing-Strategien ein Anspruchsniveau um die Suche nach weiteren Entscheidungsalternativen zu beenden. Das dritte Kapitel beschreibt wie solche Strategien in der ökonomischen Literatur als Präferenz modelliert werden um Entscheidungen zu erklären die der Nutzenmaximierung unterlegen sind während die Kognitionswissenschaften diese Strategien als Lösungen für Inferenzprobleme betrachten. Wir erklären die Divergenz mit unterschiedlichen Annahmen über die vorliegenden Informationen der Agenten. Das letzte Kapitel untersucht Satisficing-Strategien unter Taxifahrern. Wir stellen fest, dass die Stundenlöhne von Taxifahrern kaum vorhersagbar sind und ihre Entscheidungen Schichten zu beenden am besten durch einfache Satisficing-Strategien vorhergesagt werden können.
... They explain that, based on the less-is-more effect (Gigerenzer, 2008), experienced managers learn the right information to be ignored. Another example of expert auditors that are not influenced by irrelevant information leads to the same conclusion (Shanteau, 1992, as cited in Astebro and Elhedhli, 2006). Both meanings of the word experience are conditions for the generation of heuristics: direct experience of the negative outcome (experiencing) and experience as prolonged exposure to the field (know-how). ...
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Purpose This paper proposes a theory-based process model for the generation, articulation, sharing and application of managerial heuristics, from their origin as unspoken insight, to proverbialization, to formal or informal sharing, and to their adoption as optional guidelines or policy. Design/methodology/approach A conceptual paper is built using systematic and non-systematic review of literature. This paper employs a three-step approach to propose a process model for the emergence of managerial heuristics. Step one uses a systematic review of empirical studies on heuristics in order to map extant research on four key criteria and to obtain, by flicking through this sample in a moving-pictures style, the static stages of the process; step two adapts a knowledge management framework to yield the dynamic aspect; step three assembles these findings into a graphical process model and uses insights from literature to enrich its description and to synthesize four propositions. Findings The paper provides insights into how heuristics originate from experienced managers confronted with negative situations and are firstly expressed as an inequality with a threshold. Further articulation is done by proverbialization, refining and adapting. Sharing is done either in an informal way, through socialization, or in a formal way, through regular meetings. Soft adoption as guidelines is based on expert authority, while hard adoption as policy is based on hierarchical authority or on collective authority. Research limitations/implications The findings are theory-based, and the model must be empirically refined. Practical implications Practical advice for managers on how to develop and share their portfolio of heuristics makes this paper valuable for practitioners. Originality/value This study addresses the less-researched aspect of heuristics creation, transforms static insights from literature into a dynamic process model, and, in a blended-theory approach, considers insights from a distant, but relevant literature – paremiology (the science of proverbs).
... Bauer et al. (2012) show that experienced bank managers know the right information to be ignored when assessing clients. A similar conclusion is cited by Åstebro and Elhedhli (2006): "expert auditors are not influenced by irrelevant information while more inexperienced auditors are." ...
Chapter
It is said that two heads are better than one, meaning that a group thinks better and makes wiser decisions than each member taken individually. For instance, the wisdom of the crowd phenomenon describes how large crowds can estimate something (the weight of an ox, in the most famous example) astonishingly close to reality, as revealed by averaging all individual responses. Boards and juries are examples of groups especially assembled for making decisions. Many organizations have understood the power of collective decisions. Group decisions have the capacity to generate commitment, motivation, and individual responsibility. However, in many companies, group decisions are difficult to make and sometimes even harder to implement. Why might that be? It is perhaps because in business, two heads are better than one only if there is a well-established group decision-making system in place. This chapter emphasizes the importance of clear assignment of roles and of delegating, discusses pitfalls in group decision-making, and ends by offering tools for collective decisions when the team works remotely.
... Bauer et al. (2012) show that experienced bank managers know the right information to be ignored when assessing clients. A similar conclusion is cited by Åstebro and Elhedhli (2006): "expert auditors are not influenced by irrelevant information while more inexperienced auditors are." ...
Chapter
There is a growing scientific literature on the role of managerial intuition in decisions: The core finding is that intuition functions best with experience. After years of knowing their industry, managers come to recognize patterns and cues that click into place. Moreover, experienced managers can simplify their decisions by knowing what criteria to ignore in their consideration. This chapter synthesizes this growing knowledge by showing managers the benefits and limits of relying on their intuition.
... Thus, the completeness of these activities that is similar to the final class assignment and has been shown to be associated with survival and other important entrepreneurial outcomes. Similarly, decision heuristics have been shown to accurately predict subsequent outcomes for early-stage ventures (Åstebro and Elhedhli 2006). ...
... Moreover, Bingham and Eisenhardt (2014) suggested that using organizational heuristics reduces the costs of searching for relevant information that would be needed for analytical decision-making procedures. In turn, others have proposed that the use of organizational heuristics contributes to the important task of forecasting technological advances (Lenz, 1985) and the impact of these advances on the core activities of an organization (Blanning and Crandall, 1979), as well as the commercial viability of early-stage ventures (Åstebro and Elhedhli, 2006), among others. Thus, whereas the original debates on heuristics in cognitive psychology pointed to the inferiority (e.g., Kahneman et al., 1982;Tversky and Kahneman, 1974) or superiority (e.g., Gigerenzer and Todd, 1999;Goldstein and Gigerenzer, 2002) of rules-of-thumb in comparison to analytical thinking, the literature on organizational heuristics in organization and management research has increasingly converged around Gigerenzer's (2008) work, which highlights the superiority of using simple rules-of-thumb in complex and uncertain decision situations (Loock and Hinnen, 2015), such as those typically associated with organizations (e.g., Artinger et al., 2015;Bingham and 6 Eisenhardt, 2014;Luan et al., 2019;Vuori and Vuori, 2014). ...
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Although prior research has repeatedly emphasized that organizational heuristics unfold their proclaimed “superior” outcomes only through the very use of these rules-of-thumb, we know little about how actors actually use organizational heuristics “in practice”. In this paper, we develop a practice-based understanding of organizational heuristics, one that helps to unpack the “doing” of using organizational heuristics. By drawing on practice theory, we offer a reconceptualization of organizational heuristics that does justice to the enactment of these rules-of-thumb in response to the situation at hand, which can be consequential for both “superior” and “inferior” accomplishments. Our paper extends the literature on organizational heuristics by conceptualizing this concept as “heuristics-in-use”, and by offering insights into the contributions of enacting rules-of-thumb to organizational outcomes.
... Most of the existing literature looks at start-ups and venture-backed firms in developed countries, and while some of these studies find that judges' scores have some predictive power (e.g. Astebro and Elhedhli 2006;Scott et al. 2015), they also point to the immense difficulty of identifying who will be more successful (Kerr et al. 2014;Nanda 2016). Data analysts have also started to predict which new tech firms will be successful by looking at which sub-sectors have attracted the highest investments by venture capitalists and which companies have raised funds from the top-performing investment funds in the world (Ricadela 2009;Reddy 2017). ...
... In the last decade, entrepreneurship and strategic management studies have stressed the topic of heuristics, with particular reference to the processes that affect entrepreneurial companies Eisenhardt, 2011, 2014;Guercini, 2012Guercini, , 2019Loock and Hinnen, 2015;Yuan et al., 2017). Such literature has been influenced by the debate around heuristics developed in psychology and cognitive science, in particular reproducing the comparison between an approach to heuristics as a source of error (Manimala, 1992;Busenitz and Barney, 1997), and a vision of heuristics as effective solutions to complex problems (Davis et al., 2009;Eisenhardt and Sull, 2001;Åstebro and Elhedhli, 2006;Katsikopoulos and Gigerenzer, 2013). ...
Article
This paper aims at depicting the status of research on heuristics in the international business domain to understand the main topic areas in which heuristics are addressed and delve into the types of heuristics used by managers and entrepreneurs when dealing with international business activities. These are characterized by a high degree of uncertainty, drawing attention on the adoption of heuristics for decision-making. To this aim, the paper systematically reviews studies published in top-ranked international business journals between 1997 and 2018. The systematic literature review shows that the main research areas in which heuristics are developed concern foreign market entry, post-entry international expansion, foreign market selection, and international entrepreneurship. The paper also discusses the type of heuristics that are utilized by decision-makers when dealing with the uncertainty related to the internationalization process. The results of this study show that the international business literature develops the concept of heuristics in very various ways, tending to develop a plurality of approaches, without recurring or dominant forms. It is, however, possible to identify the types of heuristics and heuristically-based decisions, and a significant subset of the analyzed studies show a positive view on their effectiveness in the uncertainty of internationalization processes.
... The conceptualization and study of BBHs go back to the heuristics research program introduced by Gigerenzer et al. (1999). Many decision makers apply fast-and-frugal heuristics (simple decision strategies or rules of thumb) in the process of making judgments (see, e.g., Astebro & Elhedhli, 2006). Research from the area of judgement and decision making reveals that decisions based on a small amount of information can come to similar or better results than decisions made using a large amount of information (Gigerenzer & Gaissmaier, 2011). ...
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How do decision makers in science use bibliometric indicators and how do they rely on the indicators? Could bibliometric indicators replace the decision makers’ judgments (partly or completely)? Bornmann and Marewski (2019) suggest that these and similar questions can be empirically answered by studying the evaluative use of bibliometrics within the heuristics research program conceptualized by Gigerenzer, Todd, and ABC Research Group (1999). This program can serve as a framework so that the evaluative usage can be conceptually understood, empirically studied, and effectively taught. In this short communication, main lines suggested by Bornmann and Marewski (2019) are summarized in a brief overview.
... Competitive selection processes are prevalent in many arenas. Entrepreneurs having to persuade investors to fund their start-ups (Astebro and Elhedhli, 2006;Scott et al., 2015), job candidates going through hiring processes and interviews (Burton and Beckman, 2007;Dahl & Klepper, 2015;Noe et al. 2017), and scientists drafting proposals to sponsor their research (Jacob & Lefgren, 2011) all face fierce competition. A core concern for any such candidate is to identify the factors affecting the probability of being selected. ...
... In field settings, decision makers often choose a course of action based on experience and intuition rather than on statistical analysis (Klein, 2017). This includes doctors classifying patients based on their symptoms (McDonald, 1996), judges setting bail amounts (Dhami, 2003) or making parole decisions (Danziger et al., 2011), and managers determining which ventures will succeed (Åstebro and Elhedhli, 2006) or which customers to target (Wübben and Wangenheim, 2008). Despite the prevalence of this approach, a large body of work shows that in many domains intuitive inferences are inferior to those based on statistical models (Meehl, 1954;Dawes, 1979;Dawes et al., 1989;Camerer and Johnson, 1997;Tetlock, 2005;Kleinberg et al., 2015Kleinberg et al., , 2017. ...
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Evaluating startups in their early stages is a complex task that requires detailed analysis by experts. While automating this process on a large scale can significantly impact businesses, the inherent complexity poses challenges. This paper addresses this challenge by introducing the Startup Success Forecasting Framework (SSFF), a new automated system that combines traditional machine learning with advanced language models. This intelligent agent-based architecture is designed to reason, act, synthesize, and decide like a venture capitalist to perform the analysis end-to-end. The SSFF is made up of three main parts: - Prediction Block: Uses random forests and neural networks to make predictions. - Analyst Block: Simulates VC analysis scenario and uses SOTA prompting techniques - External Knowledge Block: Gathers real-time information from external sources. This framework requires minimal input data about the founder and startup description, enhances it with additional data from external resources, and performs a detailed analysis with high accuracy, all in an automated manner
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This study focuses on the relationship between the number of securities (n) pre-selected for mean-variance portfolio optimization and the number of optimal securities (k). We propose a heuristic k ≈ square root (n) based on empirical research optimizing different sized (n) portfolios. That is, a sample selection of 20-30 securities should yield a portfolio of about five optimal securities, and an initial sample of 500 securities, should result in an optimal portfolio of about 22. We focus on the tangent portfolio that maximizes the return-to-risk ratio. The heuristic finds its support, rationale, and logic in the numerical properties and statistical nature of the optimization. More specifically, the heuristic seems to originate in the dynamic convergence patterns observable in many statistical processes, especially in standard deviations. It is also supported by available results in the literature. Our “square root” heuristic functions as part of the wider family of approximation around the power law, where some variables (authors, securities, people) receive a disproportionate share of a given collection of items – see, for example, Pareto’s principle, Zipf’s law, Lotka’s law, Price’s square root law, Simon’s law, etc. The heuristic provides assistance not only in anticipating the number of optimal securities chosen by the mean-variance optimizer but also in suggesting selectivity in the effort of pre-selecting securities prior to the optimization and in sharpening portfolio-based approaches to investing in general. In sum, the heuristic k ≈ sqrt(n) seems helpful at all levels of portfolio management.
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Over the past decades psychological theories have made significant headway into economics, culminating in the 2002 (partially) and 2017 Nobel prizes awarded for work in the field of Behavioral Economics. Many of the insights imported from psychology into economics share a common trait: the presumption that decision makers use shortcuts that lead to deviations from rational behaviour (the Heuristics-and-Biases program). Many economists seem unaware that this viewpoint has long been contested in cognitive psychology. Proponents of an alternative program (the Ecological-Rationality program) argue that heuristics need not be irrational, particularly when judged relative to characteristics of the environment. We sketch out the historical context of the antagonism between these two research programs and then review more recent work in the Ecological-Rationality tradition. While the heuristics-and-biases program is now well-established in (mainstream neo-classical) economics via Behavioral Economics, we show there is considerable scope for the Ecological-Rationality program to interact with economics. In fact, we argue that there are many existing, yet overlooked, bridges between the two, based on independently derived research in economics that can be construed as being aligned with the tradition of the Ecological-Rationality program. We close the chapter with a discussion of the open challenges and difficulties of integrating the Ecological Rationality program with economics.
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Thesis
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Herbert Simon, the father of the decision-making discipline, wrote that “the work of managers (…) is largely work of making decisions” (Simon, Academy of Management Perspectives 1:57–64, 1987). One would say, then, that management science should have a clear idea of how managers decide, individually and in groups. Decision-making is classically understood as a logical process that goes through analyzing the situation, generating alternatives, evaluating the possible outcome and consequences of these alternatives in light of the objectives, and choosing the best solution.
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"This paper advocates a validational process utilizing a matrix of intercorrelations among tests representing at least two traits, each measured by at least two methods. Measures of the same trait should correlate higher with each other than they do with measures of different traits involving separate methods. Ideally, these validity values should also be higher than the correlations among different traits measure by the same method." Examples from the literature are described as well as problems in the application of the technique. 36 refs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)
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A review of the literature indicates that linear models are frequently used in situations in which decisions are made on the basis of multiple codable inputs. These models are sometimes used (a) normatively to aid the decision maker, (b) as a contrast with the decision maker in the clinical vs statistical controversy, (c) to represent the decision maker "paramorphically" and (d) to "bootstrap" the decision maker by replacing him with his representation. Examination of the contexts in which linear models have been successfully employed indicates that the contexts have the following structural characteristics in common: each input variable has a conditionally monotone relationship with the output; there is error of measurement; and deviations from optimal weighting do not make much practical difference. These characteristics ensure the success of linear models, which are so appropriate in such contexts that random linear models (i.e., models whose weights are randomly chosen except for sign) may perform quite well. 4 examples involving the prediction of such codable output variables as GPA and psychiatric diagnosis are analyzed in detail. In all 4 examples, random linear models yield predictions that are superior to those of human judges. (52 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Notes that an accumulating body of research on clinical judgment, decision making, and probability estimation has documented a substantial lack of ability of both experts and nonexperts. However, evidence shows that people have great confidence in their fallible judgment. This article examines how this contradiction can be resolved and, in so doing, discusses the relationship between learning and experience. The basic tasks that are considered involve judgments made for the purpose of choosing between actions. At some later time, outcome feedback is used for evaluating the accuracy of judgment. The manner in which judgments of the contingency between predictions and outcomes are made is discussed and is related to the difficulty people have in searching for disconfirming information to test hypotheses. A model for learning and maintaining confidence in one's own judgment is developed that includes the effects of experience and both the frequency and importance of positive and negative feedback. (78 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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21 professional auditors and 11 accounting students (novices) made judgments for 32 hypothetical auditing cases that were based on 8 dimensions of accounting-related information. Analyses indicated that the experts did not differ significantly from the novices in the number of significant dimensions. When evaluating the information, however, the experts' judgments primarily reflected one source of information, with other cues having secondary impact. In comparison, no single cue was dominant for the students' judgments. It is suggested that the nonuse of information by experts does not necessarily indicate a cognitive limitation. Instead, experts may have better abilities to focus on relevant information. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Shows that the assumption of simple scalability in most probabilistic analyses of choice is inadequate on both theoretical and experimental grounds. A more general theory of choice based on a covert elimination process is developed in which each alternative is viewed as a set of aspects. At each stage in the process, an aspect is selected (with probability proportional to its weight), and all the alternatives that do not include the selected aspect are eliminated. The process continues until all alternatives but 1 are eliminated. It is shown that this model (a) is expressible purely in terms of the choice alternatives without any reference to specific aspects, (b) can be tested using observable choice probabilities, and (c) generalize the choice models of R. D. Luce (see PA, Vol. 34:3588) and of F. Restle (see PA Vol. 36:5CP35R). Empirical support from a study of psychophysical and preferential judgments is presented. Strategic implications and the logic of elimination by aspects are discussed. (29 ref.)
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This article examines decision making in living systems at the level of the organism. Ten simple decision rules, or heuristics, were implemented as computer subroutines in a simulation program designed to determine how often each would select alternatives with highest-through-lowest expected value in a series of randomly generated decision situations. The decision situations varied in their number of alternatives (2, 4, or 8) and outcomes (2, 4, or 8). Results indicated that most of the heuristics, including some which “ignored” probability information, regularly selected alternatives with highest expected value, and almost never selected alternatives with lowest expected value. Implications of this finding for motivational explanations of heuristic use—as opposed to the more popular cognitive explanations—are discussed.
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Based on eye-fixation patterns, strategies for multiattribute binary choice were classified as holistic (within an alternative) or dimensional (within an attribute across alternatives). In a task environment hospitable to both strategies, dimensional processing predominated. Even for alternatives like simple gambles, which require holistic computations, dimensional strategies were used as often as holistic ones. The dimensional strategies were augmented by two procedures that simplify the computations. These simplification procedures reduce cognitive effort at the cost of a relatively small increase in errors. However, for about half the subjects the use of these simplification procedures led to systematic violations of expected utility theory on certain choices. Both the preference for dimensional over holistic strategies and the adoption of simplifying procedures are compatible with the desire to reduce cognitive effort. We propose that strategies are selected to minimize the joint cost of errors and effort.
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Judges who had estimated the likelihood of various possible outcomes of President Nixon's trips to Peking and Moscow were unexpectedly asked to remember, or reconstruct in the event that they had forgotten, their own predictions some time after the visits were completed. In addition, they indicated whether or not they thought that each event had in fact occurred. Remembered—reconstructed probabilities were generally higher than the originally assigned probabilities for events believed to have occurred and lower for those which had not (although the latter effect was less pronounced). In their original predictions, subjects overestimated low probabilities and underestimated high probabilities, although they were generally quite accurate. Judging by their reconstructed—remembered probabilities, however, subjects seldom perceived having been very surprised by what had or had not happened. These results are discussed in terms of cognitive “anchoring” and possible detrimental effects of outcome feedback.
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REPORTS 6 STUDIES USING LABORATORY REPLICAS OF THE SITUATION IN WHICH A BEGINNING CLINICIAN OBSERVES THE DIAGNOSTIC TEST PROTOCOLS OF PATIENTS WITH VARIOUS SYMPTOMS IN ORDER TO DISCOVER THE CHARACTERISTICS OF TEST PERFORMANCE THAT DISTINGUISH PATIENTS WITH EACH SYMPTOM. NAIVE UNDERGRADUATES VIEWED A SERIES OF 45 DRAW-A-PERSON TEST DRAWINGS RANDOMLY PAIRED WITH CONTRIVED SYMPTOM STATEMENTS ABOUT THE PATIENTS WHO DREW THEM. SS "REDISCOVERED" THE SAME RELATIONSHIPS BETWEEN DRAWING CHARACTERISTICS AND SYMPTOMS AS CLINICIANS REPORT OBSERVING IN CLINICAL PRACTICE, ALTHOUGH THESE RELATIONSHIPS WERE ABSENT IN THE EXPERIMENTAL MATERIALS. THE REPORTED RELATIONSHIPS CORRESPONDED TO RATED ASSOCIATIVE STRENGTH BETWEEN SYMPTOM AND DRAWING CHARACTERISTIC.
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Notes that a major difference between historical and nonhistorical judgment is that the historical judge typically knows how things turned out. 3 experiments are described with a total of 479 college students. In Exp I, receipt of such outcome knowledge was found to increase the postdicted likelihood of reported events and change the perceived relevance of event-descriptive data, regardless of the likelihood of the outcome and the truth of the report. Ss were, however, largely unaware of the effect that outcome knowledge had on their perceptions. As a result, they overestimated what they would have known without outcome knowledge (Exp II), as well as what others (Exp III) actually did know without outcome knowledge. It is argued that this lack of awareness can seriously restrict one's ability to judge or learn from the past. (16 ref)
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Analysis of the cognitive process of inductive inference should focus on inferences drawn from nonlinear as well as linear relations. Analysis of Ss' utilization of nonlinear relations is illustrated by studying 30 Ss in the following task: (a) one cue related in a linear, the other in a nonlinear manner to a criterion; (b) the criterion partly, but not perfectly, predictable from either cue alone; and (c) the criterion perfectly predictable from appropriate utilization of both. Results indicate that Ss can improve both overall performance and nonlinear data utilization, and that performance varied with task-relevant instructions. (27 ref.)
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This paper has been concerned with the manner in which information is utilized in decision making or in judgment situations. It is shown that mathematical models provide a way of describing mental processes which would otherwise be accessible only through introspection or electro-physiological techniques. A linear model and a configurational model are described, and illustrations furnished for each. Such models make possible the testing of hypotheses concerning method of combination, individual differences in judgment ability, effects of training, personality correlates, idiographic interpretation of case materials, etc.
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The Adaptive Decision Maker argues that people use a variety of strategies to make judgments and choices. The authors introduce a model that shows how decision makers balance effort and accuracy considerations and predicts which strategy a person will use in a given situation. A series of experiments testing the model are presented, and the authors analyse how the model can lead to improved decisions and opportunities for further research.
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Venture capitalists (VCs) are considered experts in identifying high-potential new ventures—gazelles. VC-backed ventures survive at a much higher rate than those ventures backed by other sources Kunkel and Hofer 1991, Sandberg 1986 and Timmons 1994. Thus, the VC decision process has received tremendous attention within the entrepreneurship literature. Nonetheless, VC-backed firms still fail at a surprisingly high rate (20%). Moreover, another 20% of the VC's portfolio fails to provide any return to the VC. Therefore, there is room for improvement in the VC investment process.
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We derive the mean and variance of the random discounted sum when Nis uncertain, as are the Xn's. This quantity arises in applications involving random cash-flows over an uncertain number of years. One such application is R&D projects, where both the magnitude and duration of cash-flows are uncertain at the time of investment decision. Previous models have assumed cash-flow duration to be certain. We relax this assumption. We then specialize these results to geometric, mixed-geometric and Poisson distributions of the cash-flow duration.
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Inventors, who are considering the commercialization of their inventions,-can often avail themselves of evaluations and advice provided by government supported programs. In Canada, such a service is provided by the Canadian Industrial Innovation Centre (CIIC), through its inventor's assistance program (IAP). In its 20 years of operation, the IAP has evaluated more than 11,000 inventions. The fee (in Canadian [Cdn.] dollars) charged for an invention evaluation was Cdn. $262 in 1995, and these fees cover about half the program's expenses. We compute the social rate of return of the Canadian IAP under different assumptions. The most plausible estimate is a social rate of return between 36% and 70%.
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Proper linear models are those in which predictor variables are given weights such that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in P. Meehl's (1954) book on clinical vs statistical prediction and research stimulated in part by that book indicate that when a numerical criterion variable (e.g., graduate GPA) is to be predicted from numerical predictor variables, proper linear models outperform clinical intuition. Improper linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method. The present article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors. In fact, unit (i.e., equal) weighting is quite robust for making such predictions. The application of unit weights to decide what bullet the Denver Police Department should use is described; some technical, psychological, and ethical resistances to using linear models in making social decisions are considered; and arguments that could weaken these resistances are presented. (50 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Given a data set about an individual or a group (e.g., interviewer ratings, life history or demographic facts, test results, self-descriptions), there are two modes of data combination for a predictive or diagnostic purpose. The clinical method relies on human judgment that is based on informal contemplation and, sometimes, discussion with others (e.g., case conferences). The mechanical method involves a formal, algorithmic, objective procedure (e.g., equation) to reach the decision. Empirical comparisons of the accuracy of the two methods (136 studies over a wide range of predictands) show that the mechanical method is almost invariably equal to or superior to the clinical method: Common antiactuarial arguments are rebutted, possible causes of widespread resistance to the comparative research are offered, and policy implications of the statistical method's superiority are discussed.
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Inventors who are considering whether and how to commercialize their inventions. can often avail themselves to evaluations and advice provided by government supported programs initiated to encourage innovation. In Canada. such a service is provided by the Canadian Innovation Centre. through its Inventor's Assistance Program. In its twenty years of operations the IAP has evaluated 11,000 inventions. The fee charged for an invention evaluation was Cdn. $262 in 1995, and these fees covered about half the program's expenses. We compute the expected value of the information the IAP's evaluation provides under different assumptions and scenarios. Under plausible conditions, the value of the information to the inventor is found to be higher than both the fee and the social cost of the program. The implications of some inventors not following the advice given to them by the IAP are briefly explored.
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Deep-thinking humans have striven to emulate deep thought in the machines being built, creating artificial intelligences and decision tools that mimic or enhance the human mind. Expert systems, data-mining software, artificial theorem provers, and chess-playing computers all perform elaborate calculations or process great amounts of information in an attempt to approach or exceed human decision making power. These complex decision machines are compared to simple inference heuristics. Results of these comparisons are presented and discussed.
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Recent research has investigated the information-gathering strategies that people employ as they attempt to test hypotheses. Three such strategies of information seeking were examined. Two kinds of hypothesis-confirmation strategies were considered. The first of these concerned evidence being sought to the extent that it is more likely under the hypothesis being tested than under the alternative. The second kind of hypothesis-confirmation strategy refers to the tendency to ask questions that will have the effect of making the hypothesis under test appear to be true. In addition, a third kind of strategy is a diagnosing strategy under which people prefer evidence that is most differentially probable under the hypothesis and the alternative. Important changes in methodology from past work were made, and the data supported a predominant diagnosing strategy and a less significant, but nonetheless strong and consistent, tendency to ask hypothesis-confirming questions. In addition, subjects' choice of questions made it likely that they would perceive as confirmed the specific hypotheses they were testing. This occurred even though the questions employed were not constraining. Discussion involves the strategies of information gathering and the reasons underlying them as well as the implications of these strategies for the inferences people make about their predictive abilities.
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A new method for empirical rule induction under conditions of uncertainty is described. The problem is to find the single best production rule of a fixed length for classification. Predictive value maximization (PVM), a heuristic search procedure through the hypothesis space of conjunctions and disjunctions of variables and their cutoff values, is outlined. Examples are taken from laboratory medicine, where the goal is to find the best combination of tests for making a diagnosis. Resampling techniques for estimating error rates are integrated into the PVM procedure for rule induction. Excellent results for PVM are reported on data sets previously analyzed in the AI literature using alternative classification techniques.
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Linear models which fit regression equations to clinical judgments. then use the fitted parts of judgments as "bootstrapped" judgments, have outperformed clinical judgments in many tasks. Empirically, the phenomenon has been pervasive, but general conditions for the success of bootstrapping models have never been explicitly linked to cross-study data. This link, combined with psychologically plausible evidence about the relationships between judgmental variables, shows that bootstrapping will improve judgments slightly under almost any realistic task conditions. This result allows one to apply bootstrapping blindly in cases where criterion information is missing or vague (precisely the cases where bootstrapping models are useful), and be confident that predictions are being improved. A simple comparison of bootstrapping models with equal weighting models is also made, but general conditions for relative success of those two models are not specified.
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A variety of systematic procedures have been proposed to manage the task of evaluating potential new products at various stages of the development process. Kenneth Baker and Gerald Albaum have chosen to contrast the performance of several different types of screening models using the actual evaluations of 86 new product managers employed by 76 Fortune 500 companies. Managers were briefed on six lighting products actually developed by another manufacturer, and they evaluated all six products using 33 criteria. The screening models were used to predict the success or failure of each of the product concepts, and these predictions were compared to the actual results experienced by the original manufacturer. Thus, the study provides insights on the accuracy of screening models.
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This article presents a comprehensive review of the current state of invention evaluation services in the United States. Gerald Udell critically examines technical evaluation programs at the National Institute of Standards and Technology and discusses the Preliminary Innovation Evaluation System (PIES) developed during the Oregon Innovation Center Experiment. He reports that most evaluation services using the PIES format are operating below the state of the art reached by the Oregon experiment and that the quality of some evaluations is suspect. Some of these programs appear to have adopted portions of the PIES format without considering PIES' substance and the five years of research and experimentation that went into PIES' development. Inasmuch as invention evaluation activity is now part of public policy in the United States, invention evaluation activity is likely to increase. Replication and extension of either the technical evaluation procedures of the National Institute of Standards and Technology or the PIES format should be undertaken with careful attention to past experiences.
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This paper describes the M3-Competition, the latest of the M-Competitions. It explains the reasons for conducting the competition and summarizes its results and conclusions. In addition, the paper compares such results/conclusions with those of the previous two M-Competitions as well as with those of other major empirical studies. Finally, the implications of these results and conclusions are considered, their consequences for both the theory and practice of forecasting are explored and directions for future research are contemplated.
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Many observers of expert decision makers have assumed an Information-Use Hypothesis: The amount of information used, as measured by number of significant cues, should be greater for experts than non-experts. Since prior studies consistently have shown that both expert and naive judgment can be described using few cues, the conclusion has been drawn that experts are limited decision makers. This paper takes a new look at this conclusion by reviewing recent literature on information use of experts and by presenting some new evidence. The results from five studies show that experts often have the same (or fewer) number of significant cues as novices, but that the information used is more relevant. Therefore, the amount of information used does not reflect degree of expertise; however, the type of information used does. This finding has implications for measurement of expertise, analysis of expert tasks, and generalizability of conclusions about experts.
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Previous studies of expert decision makers have concluded that experts, because of cognitive limitations, are generally inaccurate, unreliable, biased, lack self-insight, and gain little with experience. Overall, previous psychological studies have painted a rather bleak picture of the decision-making abilities of experts. The research reviewed here provides a different view of experts in two respects. First, expert decision makers have been found to use strategies, such as reliance on group feedback, willingness to make adjustments, and a divide-and-conquer approach, which help them overcome the effects of cognitive limitations. Second, top decision makers in agriculture, personnel selection, health care, accounting/auditing, and management have been observed to share psychological characteristics such as perceptiveness, communication skills, self-confidence, and creativity under stress. These findings have implications for (1) image and expectations of experts, (2) classifying different types of experts, (3) training and/or selecting novices to become experts, and (4) design of expert systems.
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The classification rules induced by machine learning systems are judged by two criteria: their classification accuracy on an independent test set (henceforth "accuracy"), and their complexity. The relationship between these two criteria is, of course, of keen interest to the machine learning community. There are in the literature some indications that very simple rules may achieve surprisingly high accuracy on many datasets. For example, Rendell occasionally remarks that many real world datasets have "few peaks (often just one) " and so are "easy to learn" (Rendell & Seshu, 1990, p.256). Similarly, Shavlik et al. (1991) report that, with certain qualifications, "the accuracy of the perceptron is hardly distinguishable from the more complicated learning algorithms " (p.134). Further evidence is provided by studies of pruning methods (e.g. Buntine & Niblett, 1992; Clark & Niblett, 1989; Mingers, 1989), where accuracy is rarely seen to decrease as pruning becomes more severe (for example, see Table 1) 1. This is so even when rules are pruned to the extreme, as happened with the "Err-comp " pruning method in Mingers (1989). This method produced the most accurate decision trees, and in four of the five domains studied these trees had only 2 or 3 leaves
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This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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The proposition is advanced that knowledge about the mere number of positive and negative attributes possessed by a brand is an important component of a consumer's knowledge structure. A series of experiments is presented that illustrates the unique character of frequency knowledge and its potential role in decision making. In general, it is shown that frequency knowledge can influence judgment and choice, particularly when other types of information have been poorly encoded, poorly remembered, or poorly understood.
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Professionals are frequently consulted to diagnose and predict human behavior; optimal treatment and planning often hinge on the consultant's judgmental accuracy. The consultant may rely on one of two contrasting approaches to decision-making--the clinical and actuarial methods. Research comparing these two approaches shows the actuarial method to be superior. Factors underlying the greater accuracy of actuarial methods, sources of resistance to the scientific findings, and the benefits of increased reliance on actuarial approaches are discussed.
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Analysis of decision making under risk has been dominated by expected utility theory, which generally accounts for people's actions. Presents a critique of expected utility theory as a descriptive model of decision making under risk, and argues that common forms of utility theory are not adequate, and proposes an alternative theory of choice under risk called prospect theory. In expected utility theory, utilities of outcomes are weighted by their probabilities. Considers results of responses to various hypothetical decision situations under risk and shows results that violate the tenets of expected utility theory. People overweight outcomes considered certain, relative to outcomes that are merely probable, a situation called the "certainty effect." This effect contributes to risk aversion in choices involving sure gains, and to risk seeking in choices involving sure losses. In choices where gains are replaced by losses, the pattern is called the "reflection effect." People discard components shared by all prospects under consideration, a tendency called the "isolation effect." Also shows that in choice situations, preferences may be altered by different representations of probabilities. Develops an alternative theory of individual decision making under risk, called prospect theory, developed for simple prospects with monetary outcomes and stated probabilities, in which value is given to gains and losses (i.e., changes in wealth or welfare) rather than to final assets, and probabilities are replaced by decision weights. The theory has two phases. The editing phase organizes and reformulates the options to simplify later evaluation and choice. The edited prospects are evaluated and the highest value prospect chosen. Discusses and models this theory, and offers directions for extending prospect theory are offered. (TNM)
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The impact of 36 innovation, technology, and market characteristics on the probability that early stage R&D projects will reach the market is examined. Analysis is based on data from 561 R&D projects developed by technological entrepreneurs. Four characteristics stand out as most predictive: expected profitability, technological opportunity, development risk, and appropriability conditions. These predict future commercial success well with an out-of-sample accuracy of 80.9 %. This model performs better than R&D managers' and venture capital's (VC's) forecasts of success and has the potential to be used as a screening tool for early stage R&D investment reviews by, for example, VCs. Implications for both research and practice are discussed.
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People and other animals are very adept at categorizing stimuli even when many features cannot be perceived. Many psychological models of categorization, on the other hand, assume that an entire set of features is known. We present a new model of categorization, called Categorization by Elimination, that uses as few features as possible to make an accurate category assignment. This algorithm demonstrates that it is possible to have a categorization process that is fast and frugal--using fewer features than other categorization methods--yet still highly accurate in its judgments. We show that Categorization by Elimination does as well as human subjects on a multi-feature categorization task, judging intention from animate motion, and that it does as well as other categorization algorithms on data sets from machine learning. Specific predictions of the Categorization by Elimination algorithm, such as the order of cue use during categorization and the time-course of these ...