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Cognitive Heuristics and Feedback in a Dynamic Decision Environment
Abstract
Research on cognitive processes in decision making has identified heuristics that often work well but sometimes lead to serious errors. This paper presents an investigation of the performance of heuristics in a complex dynamic setting, characterized by repeated decisions with feedback. There are three components: (1) A simulated task resembling medical decision problems (diagnosis and treatment) is described. (2) Computer models of decision strategies are developed. These include models based on cognitive heuristics as well as benchmark strategies that indicate the limit of the heuristic strategies' performance. The upper benchmark is based on statistical decision theory, the lower one on random trial and error. (3) Selected task characteristics are systematically varied and their influence on performance evaluated in simulation experiments. Results indicate that task characteristics often studied in past research (e.g., symptom diagonosticity, disease base-rates) have less influence on performance relative to feedback-related aspects of the task. These dynamic characteristics are a major determinant of when heuristics perform well or badly. The results also provide insights about the costs and benefits of various cognitive heuristics. In addition, the possible contribution of this research to the design and evaluation of decision aids is considered.
... The building design process can be described as a process of exploration where the designers search for, identify, choose, assemble and specify a design within a space of possible solutions (Logan andSmithers 1993, Gero 1998). However, humans, like the designer, have limited cognitive abilities to process large and complex networks of consequences that one solution may have compared to another (Miller 1956, Kleinmuntz 1985, Simon 1991. Without the support of tools to present and externalize the design intent and consequences, designers will need to rely on their internal mental abilities to assemble the solutions into a design. ...
... Dorst (1996) and Simon (1957) pointed out that this may lead to difficulties to handle the complexity of space resorting to using heuristics techniques, like satisficing, where people tend to select the first and apparent option (Simon 1957) in difficult decision-making situations. However, it has been reported that satisficing often leads to poor performance solutions (Kleinmuntz 1985). ...
... These flaws add to Dorst's (1996) observation that designers still succumb to satisficing, and, in our case, they did so even when mediated by BIM-tools. Moreover, this reaffirms Kleinmuntz's (1985) observation that satisficing can cause inaccuracies and flaws, like the one observed with the solution related to photovoltaic panels that did not provide valid results that would work within a Danish context. ...
Building Information Modelling (BIM) is said to hold potential for increasing efficiency of the design processes in the building industry. However, designers struggle at times to apply the different BIM-tools. In order to understand this disjoint, it is necessary to understand first the existing practices of different specialists in the building design process in order to improve future development and implementation of BIM. The aim of this article is to investigate the consequences of using BIM-tools in a collaborative building design setting consisting of different specialists. A case study was carried out to trace when BIM-tools were used (or not) in an inter-organizational design process of a naval rescue station in Denmark. The design process was holistically examined through the lens of Activity Theory which is an analytical framework. Five key findings were identified: the mediating role of 3D visuals, real-world coupling with point cloud, rule-breaking to ensure design completion, inability to integrate BIM-analysis into the design and the use of heuristics to form and choose among design solutions.
... If local-area policy-makers and their stakeholders are not able to develop an interpretative scheme to analyze results, the collective process of sensemaking [59], which is the basis of learning, fails to frame the causal structure underlying local-area performance dynamics. This is because decision-makers' mental models are based on "probabilistic" cognitive heuristics [60], which prove inadequate when complexity conditions far exceed their operationalization capacity [61,62]. "Learning in and about complex systems" [63] requires that performance management methods and tools disentangle the causality tying local-area performance with the underlying system structure that can be held responsible for the observed dynamics. ...
A local area configures a socioeconomic system in which several institutions interact. As stakeholders hold different values and perhaps conflicting interests, managing local area performance is a dynamic and complex issue. In these inter-institutional settings, performance management may help address such complexity. Traditional performance management approaches, mostly based on static and linear analysis, fail to capture the dynamic complexity of local-area performance, bounding decision-makers' mindsets to an organizational view of performance. Overcoming such limitations requires methods oriented to grasp a better understanding of the social reality in which their institutions operate. This contribution aims to illustrate how the Dynamic Performance Management (DPM) approach may foster a "performative" (We are indebted to Maria Cleofe Giorgino and Federico Barnabè, who illustrated the "performative" potential of "calculative practices" at the Workshop "Le sfide della sostenibilità. Profili aziendali e giuridici" organized by the Department of Business and Law Studies at the University of Siena, 11 October 2023. Such a perspective on the "performativity" of performance information use has remarkably enriched the conceptual positioning of our paper) use of performance information by decision-makers in inter-institutional settings. To this end, the article highlights the importance of designing conducive learning settings (i.e., action research enhanced by a system dynamics-based interactive learning environment) to support decision-makers make such a cognitive leap. Drawing from empirical evidence on destination governance studies, the article shows that enriching performance management with system dynamics modeling may help decision-makers to reflect on key issues impacting local area development, sparking a discussion on potential actions to balance economic, social, and competitive dimensions of performance. Findings reveal that DPM insight modeling holds explanatory and communicative potential in real forums by providing decision-makers with an understanding of the means-end relationships linking strategic resources to outcomes through value drivers. The use of such performance information can help local area stakeholders to (re)conceptualize the social reality in which their institutions operate. By acting as a "maieutic machine", DPM fosters a shift from an organizational and static to an inter-organizational and dynamic view of local area performance. Implications of the study include the opportunity to provide training to strengthen the active use of performance information by decision-makers in inter-institutional settings.
... In the realm of decision making and deception-related research, various studies have explored complementary themes. These include investigations into cognitive heuristics and feedback within dynamic decision-making environments [42], as well as the pivotal role of expertise and decision support tools for interviewers engaged in criminal investigations [43]. Furthermore, researchers have undertaken the development of a concise iteration of the Multidimensional Personality Questionnaire aimed at measuring personality traits associated with psychopathy [44], and they have delved into the intricate intersection of neuroscience and its application in legal decision-making processes [45]. ...
Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions.
... By adopting such a framework, laboratory researchers would more closely approximate real-world decision making. Using a simulated continuous and dynamic laboratory task environment, D. N. Kleinmuntz (1985Kleinmuntz ( , 1987 and D. N. Kleinmuntz and Thomas (1987) did indeed demonstrate that the use of this framework can lead to new insights about decision making. ...
This review begins with a discussion of Meehl's (1957) query regarding when to use one's head (i.e., intuition) instead of the formula (i.e., statistical or mechanical procedure) for clinical prediction. It then describes the controversy that ensued and analyzes the complexity and contemporary relevance of the question itself. Going beyond clinical inference, it identifies select cognitive biases and constraints that cause decision errors, and proposes remedial correctives. Given that the evidence shows cognition to be flawed, the article discusses the linear regression, Bayesian, signal detection, and computer approaches as possible decision aids. Their cost–benefit trade-offs, when used either alone or as complements to one another, are examined and evaluated. The critique concludes with a note of cautious optimism regarding the formula's future role as a decision aid and offers several interim solutions.
... Specifically, if the challenge for policymakers is understanding how human activities cause disruption and disturbance in several ecosystems (Feist et al., 2020;Meadows et al., 1972Meadows et al., , 1974Welsh, 2010), the support provided by a facilitator is a vehicle to foster policy learning. Such methodological support may challenge decision-makers mental models, whose "probabilistic" cognitive heuristics (Kleinmuntz, 1985) is inadequate when uncertainty conditions far exceed their operationalization capacity (Simon, 1947). Misperceiving the causality underlying complex issues has been proven to be a significant cause of policy failures (Forrester, 1969;Moxnes, 2004;Moxnes and Saysel, 2009;OECD, 2017;Sterman, 1989), policy resistance or unexpected outcomes (Sterman, 2000). ...
Purpose
This study aims to illustrate how collaborative platforms may leverage active community for climate change adaptation to implement biodiversity preservation policies.
Design/methodology/approach
This study adopts the Dynamic Performance Governance methodological framework to analyze the causal relationships affecting biodiversity preservation policy outcomes.
Findings
Active community reduces harmful factors for biodiversity (i.e. biological threats and anthropogenic pressure), limiting the risk of extinction of perennial plants. Stakeholders’ prior knowledge is an enabling condition of climate adaptation processes as it triggers the adoption of prescriptions and cultural changes in a community.
Practical implications
The study provides methodological guidance to define measures to deliver material information to support environmental performance governance. It elaborates an inventory of short- and long-term performance indicators integrating natural-science targets into accounting measures that can support policymakers operating in other contexts to implement climate change adaptation policies.
Social implications
As a response to the study findings, social implications provide insights into how active community in collaborative platforms for climate change may support stakeholders to address natural resources imbalances, define strategies to share the burden among them and intervene on multiple policy domains (e.g. financial, environmental and social).
Originality/value
Climate change adaptation challenges are conceptualized as “super wicked problems,” and the collaborative platforms designed to address them are rendered as complex adaptive systems. This makes the paper go beyond traditional environmental governance, demonstrating that stakeholders’ interactions within collaborative platforms harness active community specialized knowledge.
... Subsequently, their employment of procedural skills provides information to the offender to inform the further application of their perceptual skills, and so on. This recursive process is critical to reacting and responding in rapidly changing, dynamic environments (see Kleinmuntz, 1985) in which carjackers find themselves, and may occur multiple times during the execution of the offense. Such dynamic, cognitive feedback loops (see Brehmer, 1990) allow offenders to refine and alter the decision-making processes as they engage in each phase of a decision-making process that occur under extreme duress and in very short periods of time. ...
... Unfortunately, it is difficult to learn because the result is not an adequate indicator of the quality of the decision. The question of how much experts learn from feedback is documented and discussed at large in Hogarth (1978, 1981), Kleinmuntz (1985Kleinmuntz ( , 1993, Axelrod and Cohen (2000). They point out that it is very difficult for an expert to assess his/her judgmental accuracy in most real settings. ...
This paper is devoted to the role and use of experts’ knowledge for decision making. It is shown that experts’ advices must be questioned because they disagree on many things in their field of expertise and are not immune of bias and conflict of interest. In few words, experts are generally not good decision makers and it is worst when they are assembled in a panel. The COVID-19 crisis provides many examples. Then we discuss how decision makers would use experts’ knowledge.
... Cooksey, 1996;Hoch and Schkade, 1996;Melone et. al., 1995;Blattberg and Hoch, 1990;Kleinmuntz, 1985;Einhorn, 1972) bears on technology as representation, whereas work in human-computer interaction (e.g. Davern, 1997;Gerlach and Kuo, 1991;Norman, 1986;Card et. ...
... Our decision environment is very dynamic in nature, providing almost continuous feedback on sales revenues, lost sales, production, and transportation costs and relies on making decisions in distinct domains such as transportation mode, production batch size, and ordering point. In addition, some actions, such as investment, are irreversible, which limits subjects' ability to take corrective action (Kleinmuntz, 1985). Finally, investment decisions are made based on one's prediction of its impact on profitability. ...
We study how increased complexity in terms of increased stock-keeping units and/or markets can affect operational performance, with an emphasis on managerial decision-making. Specifically, when given the option to increase profits by increasing the number of markets served, we ask whether managers can increase profits by exercising this option or does increased complexity become a burden? We conduct a human-subjects experiment in which subjects manage a simulated supply chain across different levels of complexity, either as individuals or as part of a team. Subjects receive initial training in supply chain management and participate twice in the simulation—once as an individual and once as a team, while also varying complexity across trials. We show that as complexity increases, revenues also increase. However, average performance often deteriorates and many subjects destroy value, despite the increased opportunities for profit. We argue that managers are tempted to chase new revenue sources without understanding the costs or risks to future profits. In a follow-up experiment, we show that when subjects are reminded about the importance of opportunity costs, revenue declines but earnings are the same or higher. Lastly, our experiments show that both teamwork and experience increase performance and reduce the variance of earnings. Experienced teams make better investment decisions. Less experienced individuals focus on revenue rather than earnings.
... Since a long-term participatory process (Gunderson & Holling, 2002) is involved in SSRA, iterative and two-way learning between participants is critical (Chase et al., 2004;Johnson et al., 2004). Stakeholders involved in different stages may differ and the real outcomes of decisions will emerge after some time, so the stakeholders / decision makers must respond appropriately to the policy results of preceding procedures (Kleinmuntz, 1985;Kleinmuntz & Schkade, 1993). In practical terms, it is critical for stakeholders of high participation levels to be active from early to late stage. ...
... Th e question of how much experts learn from feedback is documented and discussed at large in Hogarth (1978, 1981), Kleinmuntz (1985Kleinmuntz ( , 1993, and Axelrod and Cohen (2000). Th ey point out that it is very diffi cult for experts to assess their judgmental accuracy in most real settings. ...
Human decision making is a complex process that is influenced by myriad interacting factors internally from the individual and externally from the environment. When the external context presents wicked problems that are incomplete, contradictory, and changing, such as the COVID-19 pandemic, decision makers seek to reduce risk to themselves and others by consulting experts, creating groups for guidance, acquiring data and analytics, and using decision aids. In this chapter , we explore decision making under stressful conditions and the way that decision support technologies such as analytics can assist decision makers. Because COVID-19 is an unknown disease and much is still uncertain, policy makers and decision makers have relied on the opinions of experts and expert committees , so we examine their expertise and judgment, because our actions have been guided in part by their advice. We provide examples of decision making in 183
... This indicates that providing more feedback information to humans does not necessarily lead to better estimations and decisions. The importance of outcome feedback in counteracting certain behavioral biases (through continuous "monitoring of progress through a judgment-action-outcome loop" [67] and offering corrective adjustments) has been presented before [68] yet may not apply equally to every type of bias. ...
Successful design of human-in-the-loop control systems requires appropriate models for human decision makers. Whilst most paradigms adopted in the control systems literature hide the (limited) decision capability of humans, in behavioral economics individual decision making and optimization processes are well-known to be affected by perceptual and behavioral biases. Our goal is to enrich control engineering with some insights from behavioral economics research through exposing such biases in control-relevant settings. This paper addresses the following two key questions: 1) How do behavioral biases affect decision making? 2) What is the role played by feedback in human-in-the-loop control systems? Our experimental framework shows how individuals behave when faced with the task of piloting an UAV under risk and uncertainty, paralleling a real-world decision-making scenario. Our findings support the notion of humans in Cyberphysical Systems underlying behavioral biases regardless of -- or even because of -- receiving immediate outcome feedback. We observe substantial shares of drone controllers to act inefficiently through either flying excessively (overconfident) or overly conservatively (underconfident). Furthermore, we observe human-controllers to self-servingly misinterpret random sequences through being subject to a "hot hand fallacy". We advise control engineers to mind the human component in order not to compromise technological accomplishments through human issues.
... This study goes beyond demographic differences to understand term preference by testing the potential psychological mechanisms at play. Humans are limited in the amount of cognitive effort able to be expended at a given time, forcing people to employ heuristics, or mental shortcuts of beliefs, to lessen the resources needed to make decisions (Chaiken, 1980;Kleinmuntz, 1985). Within this decision-making perspective, the yuck factor and chemophobia can be seen as the reinforcement of the contagion heuristic, the belief that "once in contact, always in contact" (Rozin et al., 2015, pg. ...
Successful water recycling initiatives depend on public acceptance. In this study, we compared risk percpetions of water labeled as recycled or reclaimed.We recruited 1264 subjects in an online panel (Qualtrics) and randomly assigned them either treatment (recycled or reclaimed) water and asked about the contents and perceived risk. Participants in the reclaimed condition were more likely to perceive the water to have harmful ingredients compared to the recycled condition. The odds of direct use acceptance for those in the recycled condition are 1.41 times (or 41%) more likely than those in the reclaimed condition. Similar results were found for indirect uses. A major finding of this study is that terminology influences the perceived contaminants and risk of reused water. Prior studies have found strong evidence that the way reused water is communicated can influence public perception. Policy impilcations favor the use of recycled water, likely due to the positive connotation recycling has in the U.S. today..
... Cooksey, 1996;Hoch and Schkade, 1996;Melone et. al., 1995;Blattberg and Hoch, 1990;Kleinmuntz, 1985;Einhorn, 1972) bears on technology as representation, whereas work in human-computer interaction (e.g. Davern, 1997;Gerlach and Kuo, 1991;Norman, 1986;Card et. ...
... Evaluasi kelompok juga memungkinkan adanya umpan balik (feedback). Kleinmuntz (1985) menyatakan bahwa peran umpan balik dalam tugas yang dinamis penting karena keberadaannya atau ketiadaannya akan sangat mempengaruhi penilaian seseorang. Hirst dan Luckett (1992) mengungkapkan bahwa beberapa penelitian telah meneliti tentang pengaruh umpan balik terhadap kualitas penilaian. ...
Dengan menggunakan metoda eksperimen, penelitian ini menguji apakah keterlibatan manajer dalam pemilihan inisiatif strategi dapat mengurangi bias yang terjadi karena motivated reasoning dan apakah penilaian evaluasi yang dilakukan secara kelompok lebih dapat mengurangi bias tersebut dibandingkan jika penilaian dilakukan secara individu. Dalam penelitian di bidang psikologi, motivated reasoning terjadi ketika manajer mengevaluasi dan menginterpretasi data secara konsisten sesuai dengan preferensi mereka, hal ini mengakibatkan manajer cenderung untuk menyimpulkan evaluasi yang sesuai dengan harapan yang diinginkan. Hasil dalam penelitian ini mengindikasikan bahwa manajer yang terlibat dalam pemilihan inisiatif strategi akan memberikan penilaian lebih baik atas keberhasilan strategi dibandingkan dengan manajer yang tidak terlibat dalam pemilihan inisiatif stategi. Namun demikian, penelitian ini belum dapat memberikan dukungan secara empiris bahwa sistem evaluasi kelompok dapat mengurangi bias yang terjadi karena motivated reasoning.
Kata Kunci: Balanced Scorecard (BSC), Keterlibatan, Sistem Evaluasi Kelompok, Motivated Reasoning, Penilaian Inisiatif Strategi
... Finally, the importance of heuristics in propagating representational states in the cognitive system suggests the value of conscious attention to the maintenance and dissemination of heuristics throughout a community. While heuristics provide support for rapid cognitive processing in complex environments (Kleinmuntz, 1985), inappropriate filtering or a mismatch between the situation and the heuristics can result in "severe and systematic errors" (Tversky & Kahneman, 1974). Fortunately, heuristics are also subject to conscious design and evaluation (Gigerenzer, 2008). ...
Most requirements engineering (RE) research has been conducted in the context of structured and agile software development. Software, however, is increasingly developed in open source software (OSS) forms which have several unique characteristics. In this study, we approach OSS RE as a sociotechnical, distributed cognitive process where distributed actors “compute” requirements— i.e., transform requirements-related knowledge into forms that foster a shared understanding of what the software is going to do and how it can be implemented. Such computation takes place through social sharing of knowledge and the use of heterogeneous artifacts. To illustrate the value of this approach, we conduct a case study of a popular OSS project, Rubinius—a runtime environment for the Ruby programming language—and identify ways in which cognitive workload associated with RE becomes distributed socially, structurally, and temporally across actors and artifacts. We generalize our observations into an analytic framework of OSS RE, which delineates three stages of requirements computation: excavation, instantiation, and testing-in-the-wild. We show how the distributed, dynamic, and heterogeneous computational structure underlying OSS development builds an effective mechanism for managing requirements. Our study contributes to sorely needed theorizing of appropriate RE processes within highly distributed environments as it identifies and articulates several novel mechanisms that undergird cognitive processes associated with distributed forms of RE.
... These difficulties arise from limited cognitive capacity to respond to delayed feedback (Diehl & Sterman, 1995;Sterman, 1994) and the tendency to rely on context-specific knowledge (Gonzalez, 2004(Gonzalez, , 2005Gonzalez et al., 2003). Increased feedback delays between decisions and corresponding outcomes negatively affect long-term performance in dynamic control tasks (Einhorn & Hogarth, 1978;Gonzalez, 2005;Kleinmuntz, 1985;Sterman, 1989). Some research has concluded that people do not learn to control dynamic systems because they misperceive the feedback (Sterman, 1989), whereas others suggest that outcome feedback may be insufficient and that other levels of feedback (e.g., process feedback, or an explanation of how the outcome emerged) are needed for people to learn to control a dynamic task (Gonzalez, 2005;Kluger & DeNisi, 1996;Lerch & Harter, 2001). ...
Objective:
The aim of this manuscript is to provide a review of contemporary research and applications on dynamic decision making (DDM).
Background:
Since early DDM studies, there has been little systematic progress in understanding decision making in complex, dynamic systems. Our review contributes to better understanding of decision making processes in dynamic tasks.
Method:
We discuss new research directions in DDM to highlight the value of simplification in the study of complex decision processes, divided into experimental and theoretical/computational approaches, and focus on problems involving control tasks and search-and-choice tasks. In computational modeling, we discuss recent developments in instance-based learning and reinforcement learning that advance modeling the processes of dynamic decisions.
Results:
Results from DDM research reflect a trend to scale down the complexity of DDM tasks to facilitate the study of the process of decision making. Recent research focuses on the dynamic complexity emerging from the interactions of actions and outcomes over time even in simple dynamic tasks.
Conclusion:
The study of DDM in theory and practice continues to be a priority area of research. New research directions can help the human factors community to understand the effects of experience, knowledge, and adaption processes in DDM tasks, but research challenges remain to be addressed, and the recent perspectives discussed can help advance a systematic DDM research program.
Application:
Classical domains, such as automated pilot systems, fighting fires, and medical emergencies, continue to be central applications of basic DDM research, but new domains, such as cybersecurity, climate change, and forensic science, are emerging as other important applications.
... Of course, such adaptive behaviors might fully or partly offset the advantages of more effort per information element. 24,50 To test whether effort feedback also influences the amount of information considered, we hypothesize that H3: Effort feedback has an impact on the total amount of information searched. ...
Decision strategies and the level of cognitive effort humans devote to decision-making are highly sensitive. This study investigates the role of feedback interventions in decision aids (DAs) to direct the user’s attention and consequently increase the level of effort spent on the thinking in multi-attribute selection problems. Guided by four research hypotheses, we conducted an experiment with two groups, one with feedback enabled, the other one with it disabled, and provide post hoc click data analysis. The self-developed persuasive DA used in the experiment featured a continuous feedback mechanism based on the users investment of time. This DA led the users through a smartphone decision scenario with altering levels of complexity. Results show that normative effort feedback increases the decision maker’s willingness to spend more effort. We provide new evidence supporting the view that DAs should pay more attention to soft persuasion by guiding the decision maker towards working harder rather than only confronting the user with final recommendations.
... The existing literature on misperceptions of feedback provided methodological guidance and strengthened my confidence in this hypothesis. Recent experimental studies of problems in this category show, with few exceptions, considerable deviations from normative standards, see Bakken (1993), Brehmer (1990), Brehmer (1992, Diehl and Sterman (1995), Dörner (1990), Funke (1991), Kleinmuntz (1985), Paich and Sterman (1993), Richardson and Rohrbaugh (1990), Smith et al. (1988), andSterman (1989a);(1989b). There seems to be a general tendency that decision makers misperceive feedback in that they undervalue the importance of delays, misperceive the workings of stock and flow relationships, and are insensitive to nonlinearities that may alter the strengths of different feedback loops as the system evolves. ...
The article summarizes key insights from four laboratory experiments to study renewable resource management. The commons problem, which is widely held to be the cause of mismanagement of common renewable resources, was ruled out by the design of the experiments. Still the participants overinvested and overutilized their resources. The explanation offered is systematic misperceptions of stocks and flows and of nonlinearities. The heuristics that people apply are intendedly rational for static, now resources, but not far dynamic, stock resources. Simplifying and reframing the management problem, by focusing on net growth rates, is suggested as a means to foster the use of more appropriate heuristics. Copyright (C) 2000 John Wiley & Sons, Ltd.
... Scholars who work on this kind of models emphasize that humans are bound by cognitive capabilities, limited information and time when it comes to making the best decision and to perform the most suitable action [50]. Studies have shown that preferences might be intransitive [51] and that even simple models might outperform individuals in decision-making [52]. Behavioral modeling aims to collect all information about how people actually make decisions and how they are biased in their action selection behavior, and to incorporate this knowledge into an inevitably complex behavioral model. ...
Imagine a person visiting an urban event. At each moment in time, the person has to weigh up different possible actions and make consecutive decisions. For instance, a person might be hungry or thirsty and would therefore like to go somewhere to eat or to drink, or a person might need to go to the toilet and thus go searching for the restrooms. Other possible desires might be to go dancing or to have a rest due to exhaustion. All these examples can be seen in the context of dynamic decision-making. To be able to implement the dynamic decision-making of virtual humans living their lives in a persistent microworld, an advanced concept to solve this—in artificial intelligence research commonly called action selection problem—is required. This article focuses on an novel approach to model the activation of motivations—as an attempt to answer the recurring question of the virtual humans “What to do next?”. The novelty is to use System Dynamics, in general defined as a top-down simulation approach, from the bottom-up inside each instance of the agent population and to implement an action selection mechanism on the basis of this methodology. This approach enables us to model the dynamic decision-making of the virtual humans with stocks and flows resulting in nonlinear motivation evolution. A case study in the context of an urban event documents the application of this innovative method.
... Even most researchers use simpler metrics for the expected information from different tests (see Baron, 1985, chap. 4;Klayman, 1987;Klayman & Ha, 1987;Kleinmuntz, 1985). Two natural questions, then, are how do people select tests for their hypotheses and how do their information search strategies affect the development of their beliefs. ...
... The systemic complexity of a system (also referred to as dynamic complexity) is a result of interactions of system components in such a way that the system produces outputs that are not easily predictable. Note that there are other def Kleinmuntz, 1985;Berry and Broadbent, 1988;Sterman, 1989aSterman, , 1989bSengupta and Abdel-Hamid, 1993;Bakken, 1993). Kerstholt and Raaijmakers (1997) argue that delay may be hindering performance by preventing an accu- rate perception of the system structure, if exact delay durations are not revealed, or by inability to relate delay information to the evolving system state. ...
In an experimental research involving stock management, we test the effects of three complexity factors on game performance measure and perceived difficulty ratings, first individually at different factor levels, and then in interaction with each other. Experiments show that, with respect to the base game, only the delay factor causes worsening in player performance, with increased delay duration and delay order. Nonlinearity and feedback do not deteriorate game performance by themselves, but they become mildly significant when they exist together with delay. Players' subjective difficulty ratings also indicate strong delay effect, and no other significant factor. Game scores and subjective difficulty ratings are positively correlated (0.58). Our results show that each complexity factor has a different type and level of influence on the overall task difficulty, which has implications for designing better simulators for education and training. Copyright (c) 2015 System Dynamics Society
Decision analysis (DA) is an explicitly prescriptive discipline that separates beliefs about uncertainties from value preferences in modeling to support decision making. Researchers have been advancing DA tools for the last 60 years to support decision makers handling complex decisions requiring subjective judgments. Recently, some DA researchers and practitioners wondered whether the difficult decisions made during the COVID-19 pandemic regarding testing, masking, closing and reopening businesses, allocating ventilators, and prioritizing vaccines would have been improved with more DA involvement. With its focus on quantifying uncertainties, value trade-offs, and risk attitudes, DA should have been a valuable tool for decision makers during the pandemic. To influence decisions, DA applications require interactions with policymakers and experts to construct formal representations of the decision frame, elicit uncertainties, and assess risk tolerances and trade-offs among competing objectives. Unfortunately, such involvement of decision analysts in the process of decision making and policy setting did not occur during much of the COVID-19 pandemic. This lack of participation may have been partly because many decision makers were unaware of when DA could be valuable in helping with the challenges of the COVID-19 pandemic. In addition, decision analysts were perhaps not sufficiently adept at inserting themselves into the policy process at critical junctures when their expertise could have been helpful.
Funding: This research was partially supported by the U.S. Department of Homeland Security through the Center for Accelerating Operational Efficiency at Arizona State University.
Problem definition: We study the adherence to the recommendations of a decision support system (DSS) for clearance markdowns at Zara, the Spanish fast fashion retailer. Our focus is on behavioral drivers of the decision to deviate from the recommendation, and the magnitude of the deviation when it occurs. Academic/practical relevance: A major obstacle in the implementation of prescriptive analytics is users’ lack of trust in the tool, which leads to status quo bias. Understanding the behavioral aspects of managers’ usage of these tools, as well as the specific biases that affect managers in revenue management contexts, is paramount for a successful rollout. Methodology: We use data collected by Zara during seven clearance sales campaigns to analyze the drivers of managers’ adherence to the DSS. Results: Adherence to the DSS’s recommendations was higher, and deviations were smaller, when the products were predicted to run out before the end of the campaign, consistent with the fact that inventory and sales were more salient to managers than revenue. When there was a higher number of prices to set, managers of Zara’s own stores were more likely to deviate from the DSS’s recommendations, whereas franchise managers did the opposite and showed a weak tendency to adhere more often instead. Two interventions aimed at shifting salience from inventory and sales to revenue helped increase adherence and overall revenue. Managerial implications: Our findings provide insights on how to increase voluntary adherence that can be used in any context in which a company wants an analytical tool to be adopted organically by its users. We also shed light on two common biases that can affect managers in a revenue management context, namely salience of inventory and sales, and cognitive workload.
Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1166 .
This book presents a comprehensive review of both theories and research on the dynamic nature of human judgment and decision making (JDM). Leading researchers in the fields of JDM, cognitive development, human learning and neuroscience discuss short-term and long-term changes in JDM skills. The authors consider how such skills increase and decline on a developmental scale in children, adolescents and the elderly; how they may be learned; and how JDM skills can be improved and aided. In addition, beyond these behavioral approaches to understanding JDM as a skill, the book provides fascinating new insights from recent evolutionary and neuropsychological approaches. The authors identify opportunities for future research on the acquisition and changing nature of JDM. In a concluding chapter, eminent past presidents of the Society for Judgment and Decision Making provide personal reflections and perspectives on the notion of JDM as a dynamic skill.
This book presents a comprehensive review of both theories and research on the dynamic nature of human judgment and decision making (JDM). Leading researchers in the fields of JDM, cognitive development, human learning and neuroscience discuss short-term and long-term changes in JDM skills. The authors consider how such skills increase and decline on a developmental scale in children, adolescents and the elderly; how they may be learned; and how JDM skills can be improved and aided. In addition, beyond these behavioral approaches to understanding JDM as a skill, the book provides fascinating new insights from recent evolutionary and neuropsychological approaches. The authors identify opportunities for future research on the acquisition and changing nature of JDM. In a concluding chapter, eminent past presidents of the Society for Judgment and Decision Making provide personal reflections and perspectives on the notion of JDM as a dynamic skill.
This book presents a comprehensive review of both theories and research on the dynamic nature of human judgment and decision making (JDM). Leading researchers in the fields of JDM, cognitive development, human learning and neuroscience discuss short-term and long-term changes in JDM skills. The authors consider how such skills increase and decline on a developmental scale in children, adolescents and the elderly; how they may be learned; and how JDM skills can be improved and aided. In addition, beyond these behavioral approaches to understanding JDM as a skill, the book provides fascinating new insights from recent evolutionary and neuropsychological approaches. The authors identify opportunities for future research on the acquisition and changing nature of JDM. In a concluding chapter, eminent past presidents of the Society for Judgment and Decision Making provide personal reflections and perspectives on the notion of JDM as a dynamic skill.
This book presents a comprehensive review of both theories and research on the dynamic nature of human judgment and decision making (JDM). Leading researchers in the fields of JDM, cognitive development, human learning and neuroscience discuss short-term and long-term changes in JDM skills. The authors consider how such skills increase and decline on a developmental scale in children, adolescents and the elderly; how they may be learned; and how JDM skills can be improved and aided. In addition, beyond these behavioral approaches to understanding JDM as a skill, the book provides fascinating new insights from recent evolutionary and neuropsychological approaches. The authors identify opportunities for future research on the acquisition and changing nature of JDM. In a concluding chapter, eminent past presidents of the Society for Judgment and Decision Making provide personal reflections and perspectives on the notion of JDM as a dynamic skill.
In conclusion, rather than present a summary of the preceding chapters, we invited nine eminent past presidents of the Society for Judgment and Decision Making (SJDM) to provide personal perspectives on the concept of JDM as a dynamic skill. These scholars were not asked to comment on the chapters in this book, but rather to highlight their personal points of contact with the notion of JDM as a dynamic skill. The following perspectives offer historical accounts, and also point to future lines of research.
Shanteau describes how over the years he has highlighted the importance of training and skill acquisition in JDM, but feels “blue” that this view has not been more popular. Wallsten remembers the benefits of learning for JDM performance found in a study that he conducted 30 years ago, and confesses that he has only recently begun to revisit this important finding. Fischhoff points out that a sound understanding of the normative implications of tasks has laid a better foundation for the study of dynamically changing skills, especially in development. Levin and colleagues provide useful examples of their research on the developmental and neurological bases of JDM skills. Reyna highlights how her fuzzy trace theory taps into JDM processes that develop over time and experience, has neurological correlates, and may be evolutionarily adaptive. Baron reveals how he now finds himself in search of the developmental origins of the types of moral heuristics and biases that he has studied during his career. Hogarth shares three steps he has developed during decades of teaching decision making that can help people make better decisions. Klayman reveals that despite decades of studying learning and development of JDM, he still seeks a greater understanding of how decision makers “get that way.” Finally, Birnbaum points to the methodological factors that have limited our understanding of JDM as a skill, and presents a challenge for future researchers: to explain how and why JDM skills change. Overall, the following perspectives provide a rare glimpse of the personalized views of those who have made significant contributions to the field of human JDM.
This book presents a comprehensive review of both theories and research on the dynamic nature of human judgment and decision making (JDM). Leading researchers in the fields of JDM, cognitive development, human learning and neuroscience discuss short-term and long-term changes in JDM skills. The authors consider how such skills increase and decline on a developmental scale in children, adolescents and the elderly; how they may be learned; and how JDM skills can be improved and aided. In addition, beyond these behavioral approaches to understanding JDM as a skill, the book provides fascinating new insights from recent evolutionary and neuropsychological approaches. The authors identify opportunities for future research on the acquisition and changing nature of JDM. In a concluding chapter, eminent past presidents of the Society for Judgment and Decision Making provide personal reflections and perspectives on the notion of JDM as a dynamic skill.
This century brought interesting challenges and opportunities that derive from the way digital technology is shaping the lives of individuals and society as a whole. A key feature of many engineered systems is that they interact with humans. Rather than solely affecting humans, people often make decisions that affect the engineered system. As an example, when driving cars, people often decide to take a route that differs from that suggested by the navigation system. This information is fed back to the service provider and henceforth used when making route suggestions to other users. The analysis and design of such cyberphysical human systems (CPHSs) would benefit from an understanding of how humans behave. However, given their immense complexity, it is unclear how to formulate appropriate models for human decision makers, especially when operating in closed-loop systems (see "Summary" for an overview of this article).
Drawing on the concept of requisite complexity, we propose that mental model complexity is crucial for teams to thrive in dynamic complex environments. Using a longitudinal research design, we examined the influence of team mental model complexity on team information search and performance trajectories in a sample of 64 teams competing in a business strategy simulation over time. We found that team information search positively influences performance growth over time. More specifically, and consistent with requisite complexity, we found that mental model complexity positively influences both performance growth and information search over time, above and beyond the effects of mental model similarity and accuracy.
Product review length has been demonstrated as one of the key factors that influence the product review helpfulness. However, we have little knowledge of a deeper understanding on how consumers process the product review length to assess product review helpfulness. Anchoring on the human’s affective-cognitive model of decision-making and the prominent coping approaches, this research revisits this key issue from consumers’ affect-oriented and cognition-oriented processing perspective. Our findings show that (1) consumers apply both the affect-oriented and cognition-oriented processing to assess the helpfulness of product review, (2) a significant inverted U-shape relationship between the review length and the review helpfulness, and interestingly, (3) such a relationship is further moderated by whether the product review author has provided a response to consumers’ comments. These findings not only provide further robust evidence to the consideration of both product review length and feedback, but also suggest a refinement of the underlying mechanism on how consumers process product review length to assess product review helpfulness.
Merton’s analysis of the Law of Unintended Consequences (LUC) criticizes rational choice theory, a cornerstone of modern economics. Section 4.1 takes a brief excursion into rational choice theory and will illuminate what Merton (and his successors) were reacting to and why. Following that, we will review the prevailing cognitive explanation for psychological triggers of LUC, in four parts. Section 4.2 provides a brief history of the research collaboration by Amos Tversky and Daniel Kahneman, who pioneered the study of distortions in judgments and choices from a cognitive perspective. Section 4.3 surveys the current “catalog” of cognitive factors that provoke LUC. Section 4.4 provides an overview of parallel research on judgment errors that arise when people make predictive judgments about the dynamics of changing situations. These cognitive factors are underappreciated triggers of LUC. Section 4.5 sketches a dynamic model that explains how these cognitive factors act to distort critical decision-making. Section 4.6 closes by identifying the cognitive biases that most strongly influence the phases of the critical decision-making process presented in Chap. 2.
As the objective of a SDILE is to improve people’s decision making and learning in dynamic tasks, its design should incorporate the mechanisms to support people’s learning. Researchers in the SD community have identified three such mechanisms to be an essential part of a SDILE: (i) HCI design principles, (ii) cognitive apprenticeship theory and Gagné’s nine instructional events, and (iii) structured debriefing. We provide an overview and elaborate on the implementation of these learning inducing elements of any SDILE with the example of our developed and validated SDILE, SIADH-ILE. Also, to better assess the efficacy of SDILEs and to fully capture the decision makers’ performance in dynamic tasks, we present a five-dimensional evaluative model. Based on this newly developed evaluative model, we advance five assertions pertaining to the efficacy of debriefing-based SDILEs.
This paper explores the complementary use of system dynamics and case study research methodology for process theory development. The rationale for this is provided on the grounds of the limitations of human cognition, particularly in understanding the evolution of complex non linear systems and processes in time. This poses difficulties when attempting to arrive at causal mechanisms for phenomena of interest with some confidence. Viewing research as an evolutionary process where better explanations are continuously sought, generated, selected and retained, simulation can be of use both in increasing the range of alternatives considered and serving as a concrete background against which the selection process takes place, thus facilitating the attainment of a satisfactory level of system understanding. Modelling and simulation has the added benefit of providing a documented artifact through which conclusions are reached and consequently it allows for replication or at least a thorough review.
Technological Decision-Support System (TDSS) to aid industrial Production Management (PM) is an important element for the Computer Integrated Manufacturing and Production (CIMP). For Control Engineers, this is a problem of Convivial Control (CC). In this paper, we analyse how to implement a Distributed Intelligence Structure in the TDSS to effectuate the CC. The paper contains four parts :
In this book, we have presented a new general model for decision-making, the DMCS, focusing on DMCS dis/covering problems and the Optimisation in Changeable Spaces (OCS) as a tool for solving DMCS problems. The proposed model is a considerable departure from the traditional optimisation and decision theory framework for it incorporates human psychology and its dynamics and the possibility of restructuring the parameters of the decision problem. This aspect has never been taken into account in such a comprehensive way in traditional decision and optimisation models. Thus, the introduced models offer new possibilities to decision makers, managers and executives in solving real-world challenging decision problems effectively and efficiently. In this chapter, we sketch five potential areas for further research, development and application of the proposed model in five sections including management and game situations, artificial intelligence, knowledge extraction and competence set-related research problems.
Purpose
– The focus is the interplay of cognitive capabilities (mathematical understanding and heuristic problem solving) and learning from feedback. Furthermore, the authors analyze the role of individual factors in designing appropriate feedback systems for complex decision-making situations. Based on a learning model the purpose of this paper is to present an experimental study analyzing the feedback effectiveness in a repeated complex production planning task. Referring to individual characteristics in terms of educational background and problem solving capabilities of the decision maker the authors compare different forms of feedback systems.
Design/methodology/approach
– The authors performed four experiments bi-weekly based on a realistic production planning situation. Participants received – depending on the treatment – different types of feedback concerning the final outcomes of the production plans. For testing the hypotheses, the authors conducted ANCOVAs and additional post hoc tests for each subgroup to explore the effects of different types of feedback on the subgroups’ decision-making performance.
Findings
– The authors show that feedback information is not always helpful, but due to acquired knowledge and problem solving capabilities can even be harmful. The authors also show that, depending on the decision maker’s individual characteristics and her past performance, the type of feedback is crucial for the learning process.
Practical implications
– The study provides important information about feedback design taking individual characteristics of decision makers (educational background, work experience) into account. Applying the results of the study can increase decision-making performance and enhance learning of production planning tasks.
Originality/value
– The findings extend previous literature reporting that the performance in complex decision-making tasks depends on educational background and on the ability to cope with the phenomena of cognitive load, working memory limitations and the capability to utilize relevant heuristics to prevent information overload. Some of our results, e.g., the negative impact of non-financial feedback of high-performing economists, contradict the general findings in the literature.
The problem of maintaining productive systems is gaining more and more attention of late. As productive machinery becomes more sophisticated and complex (and expensive), it behooves a manager to ensure that the system functions as often as possible, and, if it does break down, to repair it as quickly as possible. In modern Just-In-Time-based systems, preventive maintenance is considered a vital production function. Generally, a part of each worker’s day is set aside to perform such duties on the machinery and equipment that he or she operates. The extent to which productive systems are properly maintained affects both productivity and quality.
Simulation-based decisional aids play a critical role in the education and training of managerial decision-making. In the previous chapter, we have established an empirical research-based assertion that there is an increasing need to design human-facilitated ILEs for improving managerial decision-making in dynamic tasks. This chapter is devoted to the research related to the two core threads of thinking that identify the critical factors for the design of such an interactive learning environment. The core threads are (1) dynamic decision-making (DDM) and (2) simulation-based interactive learning environments.
Many critical real world human problem solving situations take place in dynamic event-drivers environments, where the evidence comes over time and situations can change rapidly. In these situations people must amass and integrate, uncertain, incomplete and changing evidence. A major source of human error in dynamic domains seems to be a failure to revise situation assessment as new evidence comes in. This paper will be concerned with the identification of the main descriptive patterns of fixation errors and with how to build new sytems to reduce this type of error. It will also begin the process of building a theory of fixation errors.
This study uses verbal protocol analysis to identify and compare the information cues used by experts and novices during the process of performing software diagnosis tasks. Results indicate that experts, in comparison to novices, agree more often in their use of information cues and that the degree of consensus is influenced by the type of task. The results suggest that it may be necessary to develop DSSs that emphasize information cues as an important feature.
Introduction: the neurobiology of decision, reasoning and/or recognition Procedural rationality and limited rationality Decision based on recognition Recognition, reasoning and decision support Cognitive biases Conclusion Acknowledgements Bibliography
This paper reviews the results from studies concerned with how people learn to control complex dynamic systems. The results suggest that people have considerable problems with such systems, especially when there are delays in the system and when the system has a causal net structure. The results also show some typical behavior tendencies in such systems that create vicious circles that lead to failure to control the system. It is concluded that systems designers cannot rely on operators to develop the mental models they need to control complex systems, and that these make important suggestions about what kinds of aids are needed to help the operators learn the systems better.
Results of an ongoing study investigating the effect of different task feedback characteristics on human performance are reported. In a computer-assisted experiment, subjects were asked to perform a dynamic stock-adjustment control. A subject’s control action enters the system in two ways: it effects the stock to be adjusted and it feeds back on the disturbance that impinges on the system. The latter effect is varied with respect to its strength and its delay. The major finding that emerges from the experiment is that increasing strength in the feedback link (in either a positive or negative direction) worsens performance. An effect of delay length on performance could not be shown.
Although a sizable body of knowledge is prerequisite to expert skill, that knowledge must be indexed by large numbers of patterns
that, on recognition, guide the expert in a fraction of a second to relevant parts of the knowledge store. The knowledge forms
complex schemata that can guide a problem's interpretation and solution and that constitute a large part of what we call physical
intuition.
Numerous authors (e.g., Popper, 1959) argue that scientists should try to falsify rather than confirm theories. However, recent empirical work (Wason and Johnson-Laird, 1972) suggests the existence of a confirmation bias, at least on abstract problems. Using a more realistic, computer controlled environment modeled after a real research setting, subjects in this study first formulated hypotheses about the laws governing events occurring in the environment. They then chose between pairs of environments in which they could: (I) make observations which would probably confirm these hypotheses, or (2) test alternative hypotheses. Strong evidence for a confirmation bias involving failure to choose environments allowing tests of alternative hypotheses was found. However, when subjects did obtain explicit falsifying information, they used this information to reject incorrect hypotheses.
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)
Examined whether selectivity was used in the citing of evidence in research on the psychology of judgment and decision making and investigated the possible effects that this citation bias might have on the views of readers of the literature. An analysis of the frequency of citations of good- and poor-performance articles cited in the
Social Science Citation Index from 1972 through 1981 revealed that poor-performance articles were cited significantly more often than good-performance articles. 80 members of the Judgment and Decision Making Society, a semiformal professional group, were asked to complete a questionnaire assessing the overall quality of human judgment and decision-making abilities on a scale from 0 to 100 and to list 4 examples of documented poor judgment or decision-making performance and 4 examples of good performance. Ss recalled significantly more examples of poor than of good performance. Less experienced Ss in the field appeared to have a lower opinion of human reasoning ability than did highly experienced Ss. Also, Ss recalled 50% more examples of poor performance than of good performance, despite the fact that the variety of poor-performance examples was limited. It is concluded that there is a citation bias in the judgment and decision-making literature, and poor-performance articles are receiving most of the attention from other writers, despite equivalent proportions of each type in the journals. (33 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The process of vicarious functioning, by which equivalent judgments can result from different patterns of cues, is central to any theory of judgment. It is argued that both linear regression and process-tracing models capture the various aspects of vicarious functioning: the former by dealing with the ambiguities that the organism faces with regard to the substitutions and trade-offs between cues in a redundant environment, and the latter by dealing with cue search and attention. Furthermore, although the surface structures and levels of detail of the 2 models are different, it is shown that process-tracing protocols can be generated via a general additive rule. Therefore, both types of models can be capturing the same underlying process, although at different levels of generality. Two experiments in which both models are built and tested on the same data are presented. In Exp I, experienced MMPI users made diagnostic judgments of the degree of adjustment/maladjustment from MMPI profiles; in Exp II, 1 S evaluated the nutritional quality of breakfast cereals. Results are discussed with respect to (a) links between judgment, choice, and task structure; (b) rule generality and awareness; and (c) advantages of a multimethod approach. (74 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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.
Are the costs of time and effort spent on analyzing decisions outweighed by benefits? This issue was examined in the context of a competitive business game where human teams were pitted against two kinds of simple-minded arbitrary decision rules: one where rules were applied consistently ("arbitrary-consistent"); the other where rules were subject to a random component ("arbitrary-random"). The arbitrary-consistent rules outperformed, on average, 41% of human opponents, the corresponding figure for arbitrary-random being 19%. These results are discussed within the more general context of consistency in decision making which has received considerable attention in both the management and psychological literatures, albeit in the more restricted case of non-competitive and stable environments. Issues raised by the study include the use of automated and controlled baseline strategies to study decision making in complex situations, the need to develop normative guidelines for use in turbulent, competitive environments, and the multidimensional nature of the functions of decision making in organizations.
The base-rate fallacy is people's tendency to ignore base rates in favor of, e.g., individuating information (when such is available), rather than integrate the two. This tendency has important implications for understanding judgment phenomena in many clinical, legal, and social-psychological settings. An explanation of this phenomenon is offered, according to which people order information by its perceived degree of relevance, and let high-relevance information dominate low-relevance information. Information is deemed more relevant when it relates more specifically to a judged target case. Specificity is achieved either by providing information on a smaller set than the overall population, of which the target case is a member, or when information can be coded, via causality, as information about the specific members of a given population. The base-rate fallacy is thus the result of pitting what seem to be merely coincidental, therefore low-relevance, base rates against more specific, or causal, information. A series of probabilistic inference problems is presented in which relevance was manipulated with the means described above, and the empirical results confirm the above account. In particular, base rates will be combined with other information when the two kinds of information are perceived as being equally relevant to the judged case.
Artificial intelligence, long a topic of basic computer science research, is now being applied to problems of scientific, technical, and commercial interest. Some consultation programs, although limited in versatility, have achieved levels of performance rivaling those of human experts. A collateral benefit of this work is the systematization of previously unformalized knowledge in areas such as medical diagnosis and geology.
Advanced undergraduate science majors attempted for approximately 10h each to discover the laws governing a dynamic system. The system included 27 fixed objects, some of which influenced the direction of a moving particle. At a given time, any one screen of a nine-screen matrix could be observed on a plasma display screen. Confirmatory strategies were the rule, even though half the subjects had been carefully instructed in strong inference. Falsification was counterproductive for some subjects. It seems that a firm base of inductive generalizations, supported by confirmatory research, is a prerequisite to useful implementation of a falsification strategy.
The expert can and should be used as a provider of input for a mechanical combining process since most studies show mechanical combination to be superior to clinical combination. However, even in expert measurement, the global judgment is itself a clinical combination of other judgmental components and as such it may not be as efficient as a mechanical combination of the components. The superiority of mechanically combining components as opposed to using the global judgment for predicting some external criterion is discussed. The use of components is extended to deal with multiple judges since specific judges may be differentially valid with respect to subsets of components for predicting the criterion. These ideas are illustrated by using the results of a study dealing with the prediction of survival on the basis of information contained in biopsies taken from patients having a certain type of cancer. Judgments were made by three highly trained pathologists. Implications and extensions for using expert measurement and mechanical combination are discussed.
This paper explores a heuristic-representativeness-according to which the subjective probability of an event, or a sample, is determined by the degree to which it: (i) is similar in essential characteristics to its parent population; and (ii) reflects the salient features of the process by which it is generated. This heuristic is explicated in a series of empirical examples demonstrating predictable and systematic errors in the evaluation of un- certain events. In particular, since sample size does not represent any property of the population, it is expected to have little or no effect on judgment of likelihood. This prediction is confirmed in studies showing that subjective sampling distributions and posterior probability judgments are determined by the most salient characteristic of the sample (e.g., proportion, mean) without regard to the size of the sample. The present heuristic approach is contrasted with the normative (Bayesian) approach to the analysis of the judgment of uncertainty.
When unit prices were posted on separate shelf tags in a supermarket, consumer expenditures decreased by 1%. When unit prices were displayed also on an organized list, consumer savings were 3%. In addition, the list format caused a 5% increase in the market shares of store brands. The benefits to both consumers and retailers justify the cost of providing unit price information on a widespread basis.
Reports and analyzes evidence concerning assumptions incorporated in a previously designed computer program, UNDERSTAND, which incorporates a theory about human understanding of written instructions. 20 graduate students and college faculty members were presented with puzzle isomorphs, identical in formal structure but differing in "cover stories." Ss' methods of solving the puzzles were observed and analyzed in terms of their bearing on the UNDERSTAND theory. It is concluded that how Ss name the objects in a puzzle and how they structure the internal problem representation are determined by the language in which the instructions are written. As predicted by UNDERSTAND, Ss followed the naming and representation in the instructions; however, when naming or representation is awkward and places too heavy a load on short-term memory, the inconvenient naming may be discarded in favor of a more convenient form. The program assumption that the understanding process will precede attempts at solution is partly borne out by the findings, but other assumptions are not supported. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This article applies to decision making at the level of the individual human organism. Observations of real-world systems and simulations of them have suggested that many decision-making environments are extremely complex. Indeed, given the limitations on human information processing capacity derived from laboratory experiments, we should not be able to operate in them at all. The methodology developed allowed the controlled investigation of the interaction of the individuals with dynamic systems. The effect of three complexifying factors on a subject's ability to optimize total welfare in a series of computer models of a welfare administration project over time was studied. The factors of (i) number of elements in the system, (ii) connections between them and (iii) the presence or absence of random variation were realized in a 2 × 2 × 2 analysis of variance research design in which two male and two female undergraduates served as subjects in each cell. Multivariate analyses of variance performed on derived measures of performance and system control demonstrated either insignificant differences, or significant differences in the direction opposite to that predicted. The more complex systems did not always attain poorer levels of performance than simpler systems. The results suggest that revision of theories of complexity within the social sciences may be necessary.
Simulated task environments resembling medical decision problems and strategies for their solution are investigated. The tasks, which are represented within a computer, contain hypothetical symptoms, diseases, laboratory tests, and treatments, as well as their probabilistic interrelationships. Our objectives are to develop and test strategies for diagnosis and treatment of the diseases. These strategies are also implemented on a computer, and their performance in the medical decision task is evaluated. Within specific tasks, three strategies are examined: (a) an expected utility maximizer, using Bayes' theorem to combine new data with previous observations; (b) a heuristic strategy that searches for satisfactory solutions using informal rules; and (c) a generate-and-test strategy that attempts solutions by using random, trial-and-error searches. The results illustrate the trade-off between decision quality and rule complexity. Potential advantages of simple decision strategies are discussed in a cost-benefit context. Furthermore, the role of knowledge in decision making is also discussed, and the need for explicit models of inductive learning is emphasized. Finally, general implications and possible extensions are noted.
Computer uses are demonstrated for designing a hypothetical psychodiagnostic system that can function in artificially created
mental health environments. The idea is to simulate both a psychodiagnostic system and a clinician’s strategies in order to
learn about computer as well as human information processing. An inductive method for teaching humans the elements of the
diagnostic system and its solutions is also introduced.
A variety of different operations has been used in the measurement of subjective probability or uncertainty. The position developed here is that there are also various cognitive options for processing this information, including reliance on prior generator knowledge, use of stored event frequency records, simplification rules or heuristics, and systematic bases (mainly related to attribution of event causality). The extent to which the various options form the basis for uncertainty judgment is seen to depend largely upon the combination of response requirements (frequency estimation vs. probability estimation vs. prediction vs. choice) and type of uncertainty event (frequentistic vs. nonfrequentistic, internally vs. externally generated, known vs. unknown generator) comprising the task. In addition, ease of event encoding and span of events over which judgments apply may exert a moderating influence. These ideas are incorporated into a taxonomy of measurement tasks together with their implications for judgment data.
In recent years there have been several hundred studies within the rather narrowly-defined topic of information utilization in judgment and decision making. Much of this work has been accomplished within two basic schools of research, which we have labeled the “regression” and the “Bayesian” approaches. Each has its characteristic tasks and characteristic information that must be processed to accomplish these tasks. For the most part, researchers have tended to work strictly within a single approach and there has been minimal communication between the resultant subgroups of workers. Our objective here is to present a review and comparative analysis of these two approaches. Within each, we examine (a) the models that have been developed for describing and prescribing the use of information in decision making; (b) the major experimental paradigms, including the types of judgment, prediction, and decision tasks and the kinds of information that have been available to the decision maker in these tasks; (c) the key independent variables that have been manipulated in experimental studies; and (d) the major empirical results and conclusions. In comparing these approaches, we seek the answers to two basic questions. First, do the specific models and methods characteristic of different paradigms direct the researcher's attention to certain problems and cause him to neglect others that may be equally important? Second, can a researcher studying a particular substantive problem increase his understanding by employing diverse models and diverse experimental methods?
The current scientific interest in medical diagnostic problem solving originates in human judgment research among psychologists, statisticians, computer scientists, and physicians. The present state of this research is described and traced to its beginnings in the 1950s. Although the pivotal importance of the computer is emphasized, its impact on medical information management is recognized as not having been as great as in some other spheres of intelligent reasoning. But its future important role in medical diagnostic problem solving is optimistically anticipated.
Two process tracing techniques, explicit information search and verbal protocols, were used to examine the information processing strategies subjects use in reaching a decision. Subjects indicated preferences among apartments. The number of alternatives available and number of dimensions of information available was varied across sets of apartments. When faced with a two alternative situation, the subjects employed search strategies consistent with a compensatory decision process. In contrast, when faced with a more complex (multialternative) decision task, the subjects employed decision strategies designed to eliminate some of the available alternatives as quickly as possible and on the basis of a limited amount of information search and evaluation. The results demonstrate that the information processing leading to choice will vary as a function of task complexity. An integration of research in decision behavior with the methodology and theory of more established areas of cognitive psychology, such as human problem solving, is advocated.
A progress report is presented of on-going research efforts concerning human decision making under uncertainty and risk and human problem solving and learning processes on the one hand, and machine learning, large scale programming systems, and novel programming techniques on the other. There has also been interest in how humans make deductive and inductive inferences and form and optimize heuristic rules, and how machines can reach similar results. Although the vehicle of these investigations has been the game of poker, a conceptual framework has been provided that should have a fairly wide range of applicability. The models of human judgement, choice, and decision making are incorporated in a large scale complex program. They represent both descriptive and normative theories of behavior. An interactive game environment has been recently established which, besides its usefulness for experiments in game playing, enables humans to construct machine strategies “on-line” in a question answering, advice taking mode.
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.
A theory and methodology are developed for explicitly considering the cost of comparing diverse choice alternatives. The theory allows (1) explicit analytical measures of the cost of using various simplified decision strategies, and (2) predictions regarding the distribution of mistakes a consumer is likely to make when reducing decision-making effort.
Rational choice involves two guesses, a guess about uncertain future consequences and a guess about uncertain future preferences. Partly as a result of behavioral studies of choice over a twenty-year period, modifications in the way the theory deals with the first guess have become organized into conceptions of bounded rationality. Recently behavioral studies of choice have examined the second guess, the way preferences are processed in choice behavior. These studies suggest possible modifications in standard assumptions about taste and their role in choice. This paper examines some of those modifications, some possible approaches to working on them, and some complications.
The psychological principles that govern the perception of decision problems and the evaluation of probabilities and outcomes
produce predictable shifts of preference when the same problem is framed in different ways. Reversals of preference are demonstrated
in choices regarding monetary outcomes, both hypothetical and real, and in questions pertaining to the loss of human lives.
The effects of frames on preferences are compared to the effects of perspectives on perceptual appearance. The dependence
of preferences on the formulation of decision problems is a significant concern for the theory of rational choice.
Expert and novice clinicians judged the likelihood of disease alternatives and gave thinking-aloud protocols as they evaluated simulated cases of congenital heart disease. Specific combinations of cues in the patient data were manipulated to create alternate versions of a single case so that the use of critical cues could be identified. Analyses of variance of subjects' disease judgments revealed differences between expert and novice clinicians in their use of critical cues and cue combinations. Analyses of the thinking-aloud protocols revealed that clinicians with different degrees of expertise employed different interpretations of patient data cues as well as qualitatively distinct "lines of reasoning" in reaching clinical judgments.
• Proposes a framework for skill acquisition that includes 2 major stages in the development of a cognitive skill: (1) a declarative stage in which facts about the skill domain are interpreted and (2) a procedural stage in which the domain knowledge is directly embodied in procedures for performing the skill. This general framework has been instantiated in the ACT system in which facts are encoded in a propositional network and procedures are encoded as productions. Knowledge compilation is the process by which the skill transits from the declarative stage to the procedural stage. It consists of the subprocesses of composition, which collapses sequences of productions into single productions, and proceduralization, which embeds factual knowledge into productions. Once proceduralized, further learning processes operate on the skill to make the productions more selective in their range of applications. These processes include generalization, discrimination, and strengthening of productions. Comparisons are made to similar concepts from previous learning theories. How these learning mechanisms apply to produce the power law speedup in processing time with practice is discussed. (62 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
• Proposes a framework for skill acquisition that includes 2 major stages in the development of a cognitive skill: (1) a declarative stage in which facts about the skill domain are interpreted and (2) a procedural stage in which the domain knowledge is directly embodied in procedures for performing the skill. This general framework has been instantiated in the ACT system in which facts are encoded in a propositional network and procedures are encoded as productions. Knowledge compilation is the process by which the skill transits from the declarative stage to the procedural stage. It consists of the subprocesses of composition, which collapses sequences of productions into single productions, and proceduralization, which embeds factual knowledge into productions. Once proceduralized, further learning processes operate on the skill to make the productions more selective in their range of applications. These processes include generalization, discrimination, and strengthening of productions. Comparisons are made to similar concepts from previous learning theories. How these learning mechanisms apply to produce the power law speedup in processing time with practice is discussed. (62 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The attempt has been made here to scrutinize the clinical method from a systematic, methodological point of view. Lenzen's remarks concerning the partition between the subject and object were introduced in order to suggest that the clinician not be considered a reader of instruments, but an instrument to be understood in terms of a probability model. It was suggested that of two criteria for a reduction base, high reliability and communicability, the latter is difficult to achieve, not because of mere technical difficulties but because of a fundamental fact of behavior described as vicarious functioning. Brunswik's "representative design' is asserted to be the research procedure which is congruent with vicarious functioning; its applicability is demonstrated by Todd, who also demonstrated the feasibility of applying a probability model to the clinical situation.