Toward an Integrated Methodology for the Analysis of Health-Care Systems

Smallwood, Richard D.; Sondik, Edward J.; Offensend, Fred L.

doi:10.1287/opre.19.6.1300

Cited by 22 publications

(3 citation statements)

References 2 publications

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“…There are many applications of POMDPs to health-care problems. [8][9][10][11] The surveys by Monahan, 12 Cassandra, 13 and Yaylali and Ivy 14 provide reviews of this literature. The model is used to determine when local and state decision makers should issue an alert or initiate mitigation actions such as vaccination in response to a disease threat.…”

Section: Public Health Alerting and Notificationmentioning

confidence: 99%

Systems Engineering Methods for Enhancing the Value Stream in Public Health Preparedness: The Role of Markov Models, Simulation, and Optimization

2014

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Section: Public Health Alerting and Notificationmentioning

confidence: 99%

Systems Engineering Methods for Enhancing the Value Stream in Public Health Preparedness: The Role of Markov Models, Simulation, and Optimization

2014

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“…The fields of operations research and of artificial intelligence in computer science have long investigated challenges of this kind 19 – 21 . In a seminal paper, Kaelbling and colleagues 22 introduced a novel simulation problem environment (the Tiger Problem) and a model for investigating decisions in which an agent needs to develop beneficial strategies or policies thereby maximizing its expected reward 9 , 23 , 24 , when they only have partial information about the state of the environment.…”

Section: Introductionmentioning

confidence: 99%

Humans depart from optimal computational models of interactive decision-making during competition under partial information

Rusch

Doshi

Spezio

et al. 2022

Sci Rep

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Decision making under uncertainty in multiagent settings is of increasing interest in decision science. The degree to which human agents depart from computationally optimal solutions in socially interactive settings is generally unknown. Such understanding provides insight into how social contexts affect human interaction and the underlying contributions of Theory of Mind. In this paper, we adapt the well-known ‘Tiger Problem’ from artificial-agent research to human participants in solo and interactive settings. Compared to computationally optimal solutions, participants gathered less information before outcome-related decisions when competing than cooperating with others. These departures from optimality were not haphazard but showed evidence of improved performance through learning. Costly errors emerged under conditions of competition, yielding both lower rates of rewarding actions and accuracy in predicting others. Taken together, this work provides a novel approach and insights into studying human social interaction when shared information is partial.

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“…The fields of operations research and of artificial intelligence in computer science have long investigated challenges of this kind [17,18,19]. In a seminal paper, Kaelbling et al [12] introduced a novel simulation problem environment and model for investigating decisions in which an agent needs to develop beneficial strategies or policies when they only have partial information about the state of the environment.…”

Section: Introductionmentioning

confidence: 99%

Humans depart from optimal computational models of socially interactive decision-making under partial information

Rusch¹,

Doshi²,

Gläscher³

et al. 2020

Preprint

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Decision making under uncertainty and under incomplete evidence in multiagent settings is of increasing interest in decision science, assistive robotics, and machine assisted cognition. The degree to which human agents depart from computationally optimal solutions in socially interactive settings is generally unknown. Yet, this knowledge is critical for advances in these areas. Such understanding also provides insight into how competition and cooperation affect human interaction and the underlying contributions of Theory of Mind. In this paper, we adapt the well-known ‘Tiger Problem’ from artificial-agent research to human participants in single agent and interactive, dyadic settings under both competition and cooperation. A novel element of the adaptation required participants to predict the actions of their dyadic partners in the interactive Tiger Tasks, to facilitate explicit Theory of Mind processing. Compared to computationally optimal solutions, participants gathered less information before outcome-related decision when competing with others and collected more evidence when cooperating with others. These departures from optimality were not haphazard but showed evidence of improved performance through learning across sessions. Costly errors resulted under conditions of competition, yielding both lower rates of rewarding actions and lower accuracy in predicting the actions of others, compared to prediction accuracy in cooperation. Taken together, the experiments and collected data provide a novel approach and insights into studying human social interaction and human-machine interaction when shared information is partial.

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Toward an Integrated Methodology for the Analysis of Health-Care Systems

Cited by 22 publications

References 2 publications

Systems Engineering Methods for Enhancing the Value Stream in Public Health Preparedness: The Role of Markov Models, Simulation, and Optimization

Systems Engineering Methods for Enhancing the Value Stream in Public Health Preparedness: The Role of Markov Models, Simulation, and Optimization

Humans depart from optimal computational models of interactive decision-making during competition under partial information

Humans depart from optimal computational models of socially interactive decision-making under partial information

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