Approaches to Learning to Control Dynamic Uncertainty

Osman, Magda; Glass, Brian D.; Hola, Zuzana

doi:10.3390/systems3040211

Cited by 11 publications

(12 citation statements)

References 78 publications

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“…The number of participants allocated to the frequent condition was less than that of the intermittent feedback conditions, for the following reasons. Considerable prior work (Osman et al, 2015;Osman & Speekenbrink, 2011 using the same task design has shown that the pattern of responses to frequent feedback in exact same stable dynamic decision-making tasks used presently is consistent, particular when participants are exposed to the same length of training trials (i.e. 100 training trials), as is the case with the present set of experiments.…”

Section: Predictions Generated From the Modelsupporting

confidence: 56%

“…From this we introduce the Single Limited Input, Dynamic Exploratory Response Model (SLIDER, Osman, Glass, & Hola, 2015). At the heart of most computational models of dynamic decision-making is a reinforcement learning component.…”

Section: Computational Modeling Of Dynamic Decision-makingmentioning

confidence: 99%

“…The presented computational model, SLIDER, was designed to capture differences in choice strategy along the exploratory-exploitative decision making dimension Osman et al, (2015). Under financial rewards (Experiment 1), the gains condition performed optimally under full and intermittent feedback.…”

Section: Computation Modelsmentioning

confidence: 99%

“…To consider the role of reward information on decision-making, and how it may facilitate decisionmaking in general, but also under conditions of extreme uncertainty, we discuss two literatures: judgment and decision-making work on intermittent vs. frequent feedback, and incentive-based decision-making. In addition, we present theoretical and computational models of decision-making under uncertainty as a basis on which hypotheses are generated, and which also informs the model (SLIDER, Osman, Glass, & Hola, 2015) we utilize in this study.…”

Section: Introductionmentioning

confidence: 99%

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Reward and Feedback in the Control over Dynamic Events

Osman¹,

Glass²,

Hola³

et al. 2017

PSYCH

Self Cite

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On what basis do we learn to make effective decisions when faced with intermittent feedback from actions taken in a dynamic decision-making environment? In the present study we hypothesize that reward information (financial, social) may provide useful signals that can guide decision-making in these situations. To examine this, we present three experiments in which people make decisions directly towards controlling a dynamically uncertain output. We manipulate the framing of incentives (gains and losses) and the form of the incentives (financial, social), and measure their impact on decision-making performance (both in frequent and intermittent output feedback conditions). Overall, performance suffered under intermittent output feedback. Relative to social rewards, financial rewards generally improved control performance, and a gains framing (financial, social) leading to better performance than a losses framing (financial, social). To understand how rewards affect behavior in our tasks, we present a reinforcement learning model to capture the learning and performance profiles in each of our experiments. This study shows that information regarding incentives impacts the levels of exploration, the optimality of decisions-making, and the variability in the strategies people develop to control a dynamic output experienced frequently and intermittently.

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Section: Predictions Generated From the Modelsupporting

confidence: 56%

Section: Computational Modeling Of Dynamic Decision-makingmentioning

confidence: 99%

Section: Computation Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reward and Feedback in the Control over Dynamic Events

Osman¹,

Glass²,

Hola³

et al. 2017

PSYCH

Self Cite

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“…We therefore have to build upon uncertain and incomplete information. The impact of uncertainty in decision making is well described in [4,5]. Madni and Jackson [6] describe this problem in the field of resilience engineering.…”

Section: Introductionmentioning

confidence: 99%

Thinking Tracks for Multidisciplinary System Design

Bonnema

Broenink

2016

Systems

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Systems engineering is, for a large part, a process description of how to bring new systems to existence. It is valuable as it directs the development effort. Tools exist that can be used in this process. System analysis investigates existing and/or desired situations. However, how to create a system that instantiates the desired situation depends significantly on human creativity and insight; the required human trait here is commonly called systems thinking. In literature, this trait is regularly used, but information on how to do systems thinking is scarce. Therefore, we have introduced earlier twelve thinking tracks that are concrete and help system designers to make an optimal fit between the system under design, the identified issue, the user, the environment and the rest of the world. The paper provides the scientific rationale for the thinking tracks based on literature. Secondly, the paper presents three cases of application, leading to the conclusion that the tracks are usable and effective.

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A test of synergy in dynamic system control tasks.

Schultze¹,

Drewes²,

Schulz‐Hardt³

2021

Journal of Experimental Psychology: General

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Individual performance in controlling complex dynamic systems such as managing production in a company or keeping ecosystems in balance is often suboptimal. In this article, we provide the first unequivocal test of whether groups are superior to individuals when controlling dynamic systems. In addition, we test to what extent performance advantages of groups are simply the result of statistically aggregating a larger number of individual opinions and to what extent they represent true synergy attributable to within-group interaction. In 3 experiments, we compared the system control performance of interacting real groups with that of equally sized nominal groups and with individuals. We provide evidence that groups indeed perform better than individuals in dynamic system control tasks. Furthermore, in comparing real groups with nominal groups, we show that, although the majority of real groups’ performance advantage stems from statistical aggregation, there is also evidence of true synergy. Finally, we identify the mechanism by which groups achieve synergy, namely group-to-individual transfer. Discussion allows group members to exchange critical information about the system, leading to an improved individual capability to control the system, which, in turn, improves group performance.

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Approaches to Learning to Control Dynamic Uncertainty

Cited by 11 publications

References 78 publications

Reward and Feedback in the Control over Dynamic Events

Reward and Feedback in the Control over Dynamic Events

Thinking Tracks for Multidisciplinary System Design

A test of synergy in dynamic system control tasks.

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