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

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Saurabh Steixner-Kumar ◽  
Tessa Rusch ◽  
Prashant Doshi ◽  
Michael Spezio ◽  
Jan Gläscher
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Partial Information ◽  
Social Contexts ◽  
Human Interaction ◽  
Optimal Solutions ◽  
Novel Approach ◽  
Interactive Decision Making ◽  
Shared Information ◽  
Improved Performance

AbstractDecision 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.

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

2020 ◽  
Author(s):  
Saurabh Steixner-Kumar ◽  
Tessa Rusch ◽  
Prashant Doshi ◽  
Jan Gläscher ◽  
Michael Spezio
Keyword(s):  
Decision Making ◽  
Theory Of Mind ◽  
Partial Information ◽  
Single Agent ◽  
Assistive Robotics ◽  
Human Interaction ◽  
Optimal Solutions ◽  
Competition And Cooperation ◽  
Lower Accuracy ◽  
Interactive Decision Making

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.

scholarly journals Distinguishing neural correlates of context-dependent advantageous- and disadvantageous-inequity aversion

2018 ◽  
Vol 115 (33) ◽  
pp. E7680-E7689 ◽  
Author(s):  
Xiaoxue Gao ◽  
Hongbo Yu ◽  
Ignacio Sáez ◽  
Philip R. Blue ◽  
Lusha Zhu ◽  
...  
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Computational Models ◽  
Social Contexts ◽  
Inequity Aversion ◽  
Context Dependency ◽  
Anterior Cingulate ◽  
Dorsal Anterior Cingulate Cortex ◽  
Decision Making Processes ◽  
Context Dependent

Humans can integrate social contextual information into decision-making processes to adjust their responses toward inequity. This context dependency emerges when individuals receive more (i.e., advantageous inequity) or less (i.e., disadvantageous inequity) than others. However, it is not clear whether context-dependent processing of advantageous and disadvantageous inequity involves differential neurocognitive mechanisms. Here, we used fMRI to address this question by combining an interactive game that modulates social contexts (e.g., interpersonal guilt) with computational models that enable us to characterize individual weights on inequity aversion. In each round, the participant played a dot estimation task with an anonymous coplayer. The coplayer would receive pain stimulation with 50% probability when either of them responded incorrectly. At the end of each round, the participant completed a variant of dictator game, which determined payoffs for him/herself and the coplayer. Computational modeling demonstrated the context dependency of inequity aversion: when causing pain to the coplayer (i.e., guilt context), participants cared more about the advantageous inequity and became more tolerant of the disadvantageous inequity, compared with other conditions. Consistently, neuroimaging results suggested the two types of inequity were associated with differential neurocognitive substrates. While the context-dependent processing of advantageous inequity was associated with social- and mentalizing-related processes, involving left anterior insula, right dorsolateral prefrontal cortex, and dorsomedial prefrontal cortex, the context-dependent processing of disadvantageous inequity was primarily associated with emotion- and conflict-related processes, involving left posterior insula, right amygdala, and dorsal anterior cingulate cortex. These results extend our understanding of decision-making processes related to inequity aversion.

A Novel Approach for Determining Shelter Location-Allocation in Humanitarian Relief Logistics

2021 ◽  
Vol 12 (2) ◽  
pp. 52-68
Author(s):  
Panchalee Praneetpholkrang ◽  
Sarunya Kanjanawattana
Keyword(s):  
Decision Making ◽  
Disaster Response ◽  
Large Scale ◽  
Solution Process ◽  
Decision Makers ◽  
Computational Time ◽  
Optimal Solutions ◽  
Location Allocation ◽  
Novel Approach ◽  
Artificial Neural Network Ann

This study proposes a methodology that integrates the epsilon constraint method (EC) and artificial neural network (ANN) to determine shelter location-allocation. Since shelter location-allocation is a critical part of disaster response stage, fast decision-making is very important. A multi-objective optimization model is formulated to simultaneously minimize total cost and minimize total evacuation time. The proposed model is solved by EC because it generates the optimal solutions without intervention of decision-makers during the solution process. However, EC requires intensive computational time, especially when dealing with large-scale data. Thus, ANN is combined with EC to facilitate prompt decision-making and address the complexity. Herein, ANN is supervised by the optimal solutions generated by EC. The applicability of the proposed methodology is demonstrated through a case study of shelter allocation in response to flooding in Surat Thani, Thailand. It is plausible to use this proposed methodology to improve disaster response for the benefit of victims and decision-makers.

scholarly journals Designing a generic human-machine framework for real-time supply chain planning

2016 ◽  
Vol 4 (2) ◽  
pp. 69-85 ◽  
Author(s):  
Jonathan Gaudreault ◽  
Claude-Guy Quimper ◽  
Philippe Marier ◽  
Mathieu Bouchard ◽  
François Chéné ◽  
...  
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Real Case ◽  
Optimal Solutions ◽  
Basic Scheme ◽  
Interactive Decision Making ◽  
Mixed Initiative ◽  
Business Optimization

Abstract Mixed-Initiative-Systems (MIS) are hybrid decision-making systems in which human and machine collaborate in order to produce a solution. This paper described an MIS adapted to business optimization problems. These problems can usually be solved in less than an hour as they show a linear structure. However, this delay is unacceptable for iterative and interactive decision-making contexts where users need to provide their input. Therefore, we propose a system providing the decision-makers with a convex hull of optimal solutions that minimize/maximize the variables of interest. The users can interactively modify the value of a variable and the system is able to recompute a new optimal solution in a few milliseconds. Four real-time reoptimization methods are described and evaluated. We also propose an improvement to this basic scheme in order to allow a user to explore near-optimal solutions as well. Examples showing real case of how we have exploited this framework within interactive decision support software are given. Highlights A Mixed Initiative System adapted to business optimization problems is presented. Real-time reoptimization methods are described and evaluated. The system is able to recompute a new optimal solution in a few milliseconds. Improvement to this basic scheme allow a user to explore near-optimal solutions. Examples showing real case of exploiting this framework are given.

Turning building renovation measures into energy saving opportunities

2015 ◽  
Vol 33 (2) ◽  
pp. 133-149 ◽  
Author(s):  
Navid Gohardani ◽  
Tord Af Klintberg ◽  
Folke Björk
Keyword(s):  
Decision Making ◽  
Energy Saving ◽  
Design Methodology ◽  
Residential Buildings ◽  
Content Type ◽  
Novel Approach ◽  
Improved Performance ◽  
Indoor Comfort ◽  
Decision Support Process ◽  
Practical Implications

Purpose – The purpose of this paper is to promote energy saving measures concurrent with major planned renovation/refurbishment in residential buildings. Design/methodology/approach – The methodology comprises of case studies, in which the influence of various factors is identified on the overall decision making related to building renovation/refurbishment. Findings – The employed operational decision support process enables energy saving opportunities for residential buildings in conjunction with planned major renovations/refurbishments. Research limitations/implications – The research scope is confined to residential buildings in Sweden and cooperatives with tenants as the owners and governors. Practical implications – A novel approach to synergistic energy saving and renovation in residential buildings is exhibited. Social implications – The paper presents an altered viewpoint of energy renovation means for residential buildings in the built environment. Originality/value – The paper presents a novel approach for building owners to renovate a building in terms of improved performance, energy efficiency and indoor comfort in combination with planned renovations/refurbishments.

Exploring normative perceptions and misperceptions of private, environmentally impactful behaviours

2020 ◽  
Author(s):  
Elaine Gallagher ◽  
Bas Verplanken ◽  
Ian Walker
Keyword(s):  
Social Norms ◽  
Behaviour Change ◽  
Social Contexts ◽  
The Novel ◽  
Normative Influence ◽  
Change Mechanism ◽  
Novel Approach ◽  
Classical Studies ◽  
Referent Group

Social norms have been shown to be an effective behaviour change mechanism across diverse behaviours, demonstrated from classical studies to more recent behaviour change research. Much of this research has focused on environmentally impactful actions. Social norms are typically utilised for behaviour change in social contexts, which facilitates the important element of the behaviour being visible to the referent group. This ensures that behaviours can be learned through observation and that deviations from the acceptable behaviour can be easily sanctioned or approved by the referent group. There has been little focus on how effective social norms are in private or non-social contexts, despite a multitude of environmentally impactful behaviours occurring in the home, for example. The current study took the novel approach to explore if private behaviours are important in the context of normative influence, and if the lack of a referent groups results in inaccurate normative perceptions and misguided behaviours. Findings demonstrated variance in normative perceptions of private behaviours, and that these misperceptions may influence behaviour. These behaviours are deemed to be more environmentally harmful, and respondents are less comfortable with these behaviours being visible to others, than non-private behaviours. The research reveals the importance of focusing on private behaviours, which have been largely overlooked in the normative influence literature.

scholarly journals Shifting uncertainty intolerance: methylphenidate and attention-deficit hyperactivity disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alekhya Mandali ◽  
Arjun Sethi ◽  
Mara Cercignani ◽  
Neil A. Harrison ◽  
Valerie Voon
Keyword(s):  
Decision Making ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Computational Models ◽  
Risky Choice ◽  
Risk Tolerance ◽  
Healthy Population ◽  
Hyperactivity Disorder ◽  
Risky Choices ◽  
Cardinal Symptom

AbstractRisk evaluation is a critical component of decision making. Risk tolerance is relevant in both daily decisions and pathological disorders such as attention-deficit hyperactivity disorder (ADHD), where impulsivity is a cardinal symptom. Methylphenidate, a commonly prescribed drug in ADHD, improves attention but has mixed reports on risk-based decision making. Using a double-blinded placebo protocol, we studied the risk attitudes of ADHD patients and age-matched healthy volunteers while performing the 2-step sequential learning task and examined the effect of methylphenidate on their choices. We then applied a novel computational analysis using the hierarchical drift–diffusion model to extract parameters such as threshold (‘a’—amount of evidence accumulated before making a decision), drift rate (‘v’—information processing speed) and response bias (‘z’ apriori bias towards a specific choice) focusing specifically on risky choice preference. Critically, we show that ADHD patients on placebo have an apriori bias towards risky choices compared to controls. Furthermore, methylphenidate enhanced preference towards risky choices (higher apriori bias) in both groups but had a significantly greater effect in the patient population independent of clinical scores. Thus, methylphenidate appears to shift tolerance towards risky uncertain choices possibly mediated by prefrontal dopaminergic and noradrenergic modulation. We emphasise the utility of computational models in detecting underlying processes. Our findings have implications for subtle yet differential effects of methylphenidate on ADHD compared to healthy population.

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