Special Issue "Dynamic Decision Making in Controlled Experiments"

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: closed (30 September 2015)

Special Issue Editors

Guest Editor
Dr. Andreas Größler

Institute for Management Research – System Dynamics Group, Radboud University Nijmegen, The Netherlands
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Guest Editor
Dr. Hendrik Stouten

Institute for Management Research – System Dynamics Group, Radboud University Nijmegen, The Netherlands
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Special Issue Information

Dear Colleagues,

 

Decision tasks are dynamic whenever decisions made at time t alter the state of a system and, thus, the information that conditions decisions that have to be made at time t + 1. To make such repeated decisions, ideally, decision makers monitor their decisions’ outcomes and adapt further decisions to the current (or an expected future) state of the system. In fact, the decision maker and the system are entwined in feedback loops whereby decisions alter the state of the system, giving rise to new information and leading to new decisions. Besides the effects of decisions on the state of the system, autonomous changes are sometimes also experienced by decision makers, based on external influences on the system. While research so far has mostly focused on static (one-shot) decisions, it is obvious that many (or even most) crucial real-life decisions are dynamic. Continuous planning and implementation processes in companies, resource exploitation and regeneration policies in mining, agriculture, and fisheries, and economic and environmental steering by policy makers are prime examples of important dynamic decisions.

This Special Issue aims to provide an account of the current state of research in dynamic decision making, with a focus on laboratory experimental studies and articles laying the foundations for such experimental studies [1–3]. The ultimate goal of the individual papers, as well as the complete Special Issue, is to contribute to the development of a theory of dynamic decision making, i.e., a theory that addresses the questions about the antecedents, mechanisms, and success factors of dynamic decision making. In comparison to other journals, we aim for a quick procedure (the overall processing time from this announcement to the last papers published is one year; finalized papers will be published immediately online after their acceptance) and offer open access publication for all articles. We appreciate receiving contributions (as full articles—including revised conference papers, short communications or reviews) from many different fields of science: psychology, system dynamics, agent-based simulation, organization science, behavioral economics, neuro-sciences, and the wider systems sciences movement.

Topics might include but are not limited to

  • Conceptualizations of dynamic decision making and dynamic decision making environments
  • Description of experimental settings for investigating dynamic decision making in various contexts; for instance, profit vs. not-for-profit organizations, short-term vs. long-term decisions, “autocratic” vs. “democratic” decisions
  • Development of relevant measures/measurement scales to be used in dynamic decision making research (must include results of pilot testing)
  • Description of simulators to be used in dynamic decision making research (must include results of pilot testing)
  • Experimental studies on various aspects of dynamic decision making situations; for instance, stock-flow thinking, understanding of accumulations, effects of delays and non-linearities
  • Experimental studies on various aspects of decision makers in dynamic settings; for instance, the role of personality, intelligence, motivation, education, culture
  • Broadening the dynamic decision making research to cover group decision processes
  • Descriptions of research agendas to investigate dynamic decision making in the longer term, including opportunities and issues for their implementation

Dr. Andreas Größler
Dr. Hendrik Stouten
Guest Editors

References

1. Arango Aramburo, S.; Castañedo Azevedo, J.A.; Olaya Morales, Y. Laboratory experiments in the system dynamics field. Syst. Dynam. Rev. 2012, 28, 94–106.

2. Gonzalez, C.; Dutt, V. A generic dynamic control task for behavioral research and education. Comput. Hum. Behav. 2011, 27, 1904–1914.

3. Gonzalez, C. Decision support for real-time, dynamic decision-making tasks. Organ. Behav. Hum. Decis. Process. 2005, 96, 142–154.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • dynamic decision making
  • experiment
  • simulation
  • model
  • system
  • learning

Published Papers (6 papers)

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Editorial

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Open AccessEditorial Introduction to the Special Issue “Dynamic Decision-Making in Controlled Experiments”
Systems 2015, 3(2), 60-61; https://doi.org/10.3390/systems3020060
Received: 11 June 2015 / Revised: 11 June 2015 / Accepted: 11 June 2015 / Published: 18 June 2015
Cited by 1 | PDF Full-text (159 KB) | HTML Full-text | XML Full-text
Abstract
While research so far has mostly focused on static (one-shot) decisions, it is obvious that many (or even most) crucial real-life decisions are dynamic. [...] Full article
(This article belongs to the Special Issue Dynamic Decision Making in Controlled Experiments)

Research

Jump to: Editorial

Open AccessArticle Non-Conscious vs. Deliberate Dynamic Decision-Making—A Pilot Experiment
Received: 28 September 2015 / Revised: 2 February 2016 / Accepted: 17 February 2016 / Published: 24 February 2016
Cited by 1 | PDF Full-text (1279 KB) | HTML Full-text | XML Full-text
Abstract
The purpose of this paper is to explore the effects of non-conscious vs. deliberate ways of making decisions in a dynamic decision-making task. An experimental setting is used to study this question; three experimental groups are distinguished: immediate decision-making (only very limited time
[...] Read more.
The purpose of this paper is to explore the effects of non-conscious vs. deliberate ways of making decisions in a dynamic decision-making task. An experimental setting is used to study this question; three experimental groups are distinguished: immediate decision-making (only very limited time for deliberate cognitive processing), considerate decision-making (relatively long time for deliberate cognitive processing), and distracted decision-making (time for non-conscious cognitive processing only). As experimental stimulus, a simulator based on the Kaibab Plateau model was employed. With a sample size of more than 100 experimental participants, group differences are not significant for most data examined. Implications comprise the formulation of a framework to guide further research. The value of this paper lies in the fact that it connects to a recent discussion in psychology and transfers it to a domain in the core interest of the system community: decision-making in situations with dynamic complexity. Furthermore, it offers a range of improvement points for potential follow-up studies. Full article
(This article belongs to the Special Issue Dynamic Decision Making in Controlled Experiments)
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Open AccessArticle Modeling Dynamic Decision-Making of Virtual Humans
Received: 31 August 2015 / Revised: 30 November 2015 / Accepted: 17 December 2015 / Published: 15 January 2016
Cited by 2 | PDF Full-text (11056 KB) | HTML Full-text | XML Full-text
Abstract
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
[...] Read more.
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. Full article
(This article belongs to the Special Issue Dynamic Decision Making in Controlled Experiments)
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Open AccessArticle The Effect of a Structured Method on Mental Model Accuracy and Performance in a Complex Task
Systems 2015, 3(4), 264-286; https://doi.org/10.3390/systems3040264
Received: 27 August 2015 / Revised: 2 November 2015 / Accepted: 10 November 2015 / Published: 13 November 2015
Cited by 4 | PDF Full-text (932 KB) | HTML Full-text | XML Full-text
Abstract
In comparison to their performance with normative standards or even simple heuristics, humans do not perform well in complex decision-making. The application of systems thinking to help people to understand and handle interdependent and complex systems is proposed as a means of improving
[...] Read more.
In comparison to their performance with normative standards or even simple heuristics, humans do not perform well in complex decision-making. The application of systems thinking to help people to understand and handle interdependent and complex systems is proposed as a means of improving this poor performance. The aim of this study is to investigate the effect of a generic systems thinking method, i.e., a structured method, on performance. A laboratory experiment was conducted using a dynamic and complex simulation task. The results demonstrated that subjects provided with a structured method achieved a higher performance. In addition, mental model accuracy had a significant effect on performance, as already shown by several previous studies. The results of our study provide a way of teaching subjects how to improve their performance when coping with complex systems in general. This has implications for education in the fields of complex systems and system dynamics. Full article
(This article belongs to the Special Issue Dynamic Decision Making in Controlled Experiments)
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Open AccessArticle Approaches to Learning to Control Dynamic Uncertainty
Systems 2015, 3(4), 211-236; https://doi.org/10.3390/systems3040211
Received: 1 July 2015 / Accepted: 24 September 2015 / Published: 10 October 2015
Cited by 4 | PDF Full-text (538 KB) | HTML Full-text | XML Full-text
Abstract
In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains) or exploit (maximizing their short term gains)? More to the point, how does this choice of learning strategy
[...] Read more.
In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains) or exploit (maximizing their short term gains)? More to the point, how does this choice of learning strategy influence one’s later ability to control the environment? In the present study, we explore whether people’s self-reported learning strategies and levels of arousal (i.e., surprise, stress) correspond to performance measures of controlling a Highly Uncertain or Moderately Uncertain dynamic environment. Generally, self-reports suggest a preference for exploring the environment to begin with. After which, those in the Highly Uncertain environment generally indicated they exploited more than those in the Moderately Uncertain environment; this difference did not impact on performance on later tests of people’s ability to control the dynamic environment. Levels of arousal were also differentially associated with the uncertainty of the environment. Going beyond behavioral data, our model of dynamic decision-making revealed that, in actual fact, there was no difference in exploitation levels between those in the highly uncertain or moderately uncertain environments, but there were differences based on sensitivity to negative reinforcement. We consider the implications of our findings with respect to learning and strategic approaches to controlling dynamic uncertainty. Full article
(This article belongs to the Special Issue Dynamic Decision Making in Controlled Experiments)
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Open AccessArticle Effects of Structural Transparency in System Dynamics Simulators on Performance and Understanding
Systems 2015, 3(4), 152-176; https://doi.org/10.3390/systems3040152
Received: 31 August 2015 / Revised: 23 September 2015 / Accepted: 24 September 2015 / Published: 1 October 2015
Cited by 2 | PDF Full-text (953 KB) | HTML Full-text | XML Full-text
Abstract
Prior exploration is an instructional strategy that has improved performance and understanding in system-dynamics-based simulators, but only to a limited degree. This study investigates whether model transparency, that is, showing users the internal structure of models, can extend the prior exploration strategy and
[...] Read more.
Prior exploration is an instructional strategy that has improved performance and understanding in system-dynamics-based simulators, but only to a limited degree. This study investigates whether model transparency, that is, showing users the internal structure of models, can extend the prior exploration strategy and improve learning even more. In an experimental study, participants in a web-based simulation learned about and managed a small developing nation. All participants were provided the prior exploration strategy but only half received prior exploration embedded in a structure-behavior diagram intended to make the underlying model’s structure more transparent. Participants provided with the more transparent strategy demonstrated better understanding of the underlying model. Their performance, however, was the equivalent to those in the less transparent condition. Combined with previous studies, our results suggest that while prior exploration is a beneficial strategy for both performance and understanding, making the model structure transparent with structure-behavior diagrams is more limited in its effect. Full article
(This article belongs to the Special Issue Dynamic Decision Making in Controlled Experiments)
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