Dynamic Decision Making: Learning Processes and New Research Directions

González, Cleotilde; Fakhari, Pegah; Busemeyer, Jerome R.

doi:10.1177/0018720817710347

Cited by 56 publications

(59 citation statements)

References 92 publications

(140 reference statements)

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“…Most of the MoF articles also mention the term "dynamic decision making" (DDM) because the experiments expose participants to feedback-driven systems with stock-accumulation, where decisions taken at one point in time change the conditions under which decisions will be taken at later moments. Several authors carrying out DDM experiments have done so in search of inferring information about the participants' mental models (Arango et al, 2012;Gary, Pillinger, & Wood, 2012;Gary & Wood, 2011, 2016Gonzalez, Fakhari, & Busemeyer, 2017;Stouten et al, 2012;Stouten & Größler, 2017). Some researchers report that more accurate mental models have come together with increased performance in decision experiments (Capelo & Dias, 2009).…”

Section: Dynamic Decision Makingmentioning

confidence: 99%

“…(c) At the same time, enlarging the definition does not eliminate the need to define what a mental model consists of; otherwise, one can only make limited statements regarding mental model accuracy, implicitly assuming that the mental model's structure ought to be similar to the reference (simulation) model. 1 Additionally, in the field of DDM, researchers also try to understand and computationally replicate the cognitive processes underlying decisions in dynamic environments (Gonzalez, 2004(Gonzalez, , 2005a(Gonzalez, , 2005bGonzalez et al, 2017;González, Calderón, & González, 2012;Gonzalez, Lerch, & Lebiere, 2003;Gonzalez, Thomas, & Vanyukov, 2005;Gonzalez, Vanyukov, & Martin, 2005;Gonzalez & Wong, 2012). Whereas mental models are "mental" structures, the "cognitive" processes deal (a) with how humans store information on situational cues, decisions taken, and outcomes; and (b) retrieve this information in response to new situations.…”

Section: Dynamic Decision Makingmentioning

confidence: 99%

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Unrecognized interdependencies in mental models—The case for taking feedback loops into account

Schaffernicht

2019

Syst Res Behav Sci

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This article attempts to bridge two streams of research: on the one hand, system dynamics research investigates the “misperception of feedback” focusing on decision rules rather than mental models. On the other hand, mental model research in organizational and management studies does not take interdependencies into account. The article examines data from an interview‐based study with nine vineyard executives and finds that 47 of the 219 elicited variables belong to one or several of 49 feedback loops inherent in the structure of the elicited mental models. However, 44 of these loops are “dark”: They have not been recognized by the executives. Not taking loops into account increases the likelihood of misjudging the situation. It is concluded (a) that mental model research methods should account for loops in order to address the problem of dark loops (b) and that executives and researchers need elicitation tools that automatically detect and visualize loops.

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Section: Dynamic Decision Makingmentioning

confidence: 99%

Section: Dynamic Decision Makingmentioning

confidence: 99%

Unrecognized interdependencies in mental models—The case for taking feedback loops into account

Schaffernicht

2019

Syst Res Behav Sci

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“…The present investigation is of descriptive character, being a qualitative analysis that follows from the understanding of the behavioral theory of the firm of, 3,7,20 the theory of the behavior of the decisions, 17,21,22 in terms of behavioral decision-making processes and their analysis through learning, which significantly influence the process of making decisions that help mitigate rationality bounded to a certain degree due to this key factor, which is learning (Figure 2).…”

Section: Methodsmentioning

confidence: 99%

Untitled

2019

MOJCRR

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The purpose of this paper is to study the relationship between rationality and decision making. As a specific objective it will be explained the benefit of learning in the decision making process derived from its results. The research question is: How does bounded rationality impact the decision making of organizations? The initial hypothesis is that the organization, as a social system, seeks to maintain the balance between the behavior that determines the positions of people and the structure of values and beliefs shared among them, but that is bounded to the rationality of the decision maker. The research method used is descriptive, being a qualitative analysis, that follows from the understanding of the behavioral theory of the firm also the behavioral decision theory, in terms of behavioral decision-making processes and the analysis through learning. The main conclusion is that bounded rationality occurs when companies lack context information of the results of their actions, being forced to make less than optimal decisions because they have to adjust to the conditions in which they operate. Decisions involve a commitment of large amounts of resources of the organization for the fulfillment of the objectives and purposes of the organization through the appropriate means. These means can be translated into models that help reduce the limits of rationality in organizations.

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“…Dynamic decision-making problems are both widely prevalent (Brehmer, 1992;Friedman, Isaac, James, & Sunder, 2014;Gonzalez, Fakhari, & Busemeyer, 2017;March, 1996) and notoriously challenging from a computational perspective. While optimal solutions often depend on backwards induction, several studies have shown that even for shallow planning depth, humans do not use backwards induction (e.g., Hotaling & Busemeyer, 2012;Huys et al, 2015;Zhang & Yu, 2013).…”

Section: Boundedly Rational Alternativesmentioning

confidence: 99%

“…In multi‐armed bandit problems, for example, the optimal policy (i.e., Gittins index) can only be derived under restrictive assumptions (Whittle, ). Similarly, psychologists have long investigated human decision‐making in challenging dynamic tasks where it is impossible to even derive the optimal policy (Brehmer, ; Gonzalez et al., ).…”

mentioning

confidence: 99%

Make‐or‐Break: Chasing Risky Goals or Settling for Safe Rewards?

Analytis

Gelastopoulos

2019

Cognitive Science

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Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality.

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Dynamic Decision Making: Learning Processes and New Research Directions

Abstract: 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.

Cited by 56 publications

References 92 publications

Unrecognized interdependencies in mental models—The case for taking feedback loops into account

Unrecognized interdependencies in mental models—The case for taking feedback loops into account

Untitled

Make‐or‐Break: Chasing Risky Goals or Settling for Safe Rewards?

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