scholarly journals Bayesian Inference of Other Minds Explains Human Choices in Group Decision Making

2018 ◽  
Author(s):  
Koosha Khalvati ◽  
Seongmin A. Park ◽  
Saghar Mirbagheri ◽  
Remi Philippe ◽  
Mariateresa Sestito ◽  
...  
Keyword(s):  
Decision Making ◽  
Bayesian Inference ◽  
Markov Decision Processes ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Processes ◽  
Other Minds ◽  
Markov Decision ◽  
The Mind ◽  
Partially Observable

AbstractTo make decisions in a social context, humans have to predict the behavior of others, an ability that is thought to rely on having a model of other minds known as theory of mind. Such a model becomes especially complex when the number of people one simultaneously interacts is large and the actions are anonymous. Here, we show that in order to make decisions within a large group, humans employ Bayesian inference to model the “mind of the group,” making predictions of others’ decisions while also considering the effects of their own actions on the group as a whole. We present results from a group decision making task known as the Volunteers Dilemma and demonstrate that a Bayesian model based on partially observable Markov decision processes outperforms existing models in quantitatively explaining human behavior. Our results suggest that in group decision making, rather than acting based solely on the rewards received thus far, humans maintain a model of the group and simulate the group’s dynamics into the future in order to choose an action as a member of the group.

scholarly journals Modeling other minds: Bayesian inference explains human choices in group decision-making

2019 ◽  
Vol 5 (11) ◽  
pp. eaax8783 ◽  
Author(s):  
Koosha Khalvati ◽  
Seongmin A. Park ◽  
Saghar Mirbagheri ◽  
Remi Philippe ◽  
Mariateresa Sestito ◽  
...  
Keyword(s):  
Decision Making ◽  
Bayesian Inference ◽  
Group Decision Making ◽  
Group Decision ◽  
Other Minds ◽  
Group Interactions ◽  
Markov Decision ◽  
The Mind ◽  
Partially Observable ◽  
Large Groups

To make decisions in a social context, humans have to predict the behavior of others, an ability that is thought to rely on having a model of other minds known as “theory of mind.” Such a model becomes especially complex when the number of people one simultaneously interacts with is large and actions are anonymous. Here, we present results from a group decision-making task known as the volunteer’s dilemma and demonstrate that a Bayesian model based on partially observable Markov decision processes outperforms existing models in quantitatively predicting human behavior and outcomes of group interactions. Our results suggest that in decision-making tasks involving large groups with anonymous members, humans use Bayesian inference to model the “mind of the group,” making predictions of others’ decisions while also simulating the effects of their own actions on the group’s dynamics in the future.

An Introduction to Fully and Partially Observable Markov Decision Processes

2012 ◽  
pp. 33-62 ◽  
Author(s):  
Pascal Poupart
Keyword(s):  
Decision Making ◽  
Markov Decision Processes ◽  
Decision Processes ◽  
Sequential Decision Making ◽  
Decision Making Under Uncertainty ◽  
Sequential Decision ◽  
Markov Decision ◽  
The Common ◽  
Partially Observable Markov ◽  
Partially Observable

The goal of this chapter is to provide an introduction to Markov decision processes as a framework for sequential decision making under uncertainty. The aim of this introduction is to provide practitioners with a basic understanding of the common modeling and solution techniques. Hence, we will not delve into the details of the most recent algorithms, but rather focus on the main concepts and the issues that impact deployment in practice. More precisely, we will review fully and partially observable Markov decision processes, describe basic algorithms to find good policies and discuss modeling/computational issues that arise in practice.

Markov decision processes for integrating life cycle dynamics into fab-level decision making

1999 ◽  
Vol 32 (2) ◽  
pp. 4852-4857
Author(s):  
Shalabh Bhatnagar ◽  
Michael C. Fu ◽  
Steven I. Marcus ◽  
Ying He
Keyword(s):  
Decision Making ◽  
Life Cycle ◽  
Markov Decision Processes ◽  
Decision Processes ◽  
Markov Decision

scholarly journals Learning While Deciding in Groups

2019 ◽  
Author(s):  
Scott Tindale ◽  
Jeremy R. Winget
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Processes ◽  
Learning Potential ◽  
Group Members ◽  
Actual Learning

Groups are used to make many important societal decisions. Similar to individuals, by paying attention to the information available during the decision processes and the consequences of the decisions, groups can learn from their decisions as well. In addition, group members can learn from each other by exchanging information and being exposed to different perspectives. However, groups make decisions in many different ways and the potential and actual learning that takes place will vary as a function of the manner in which groups reach consensus. This chapter reviews the literature on group decision making with a special emphasis on how and when group decision making leads to learning. We argue that learning is possible in virtually any group decision making environment but freely interacting groups create the greatest potential for learning. We also discuss when and why group may not always take advantage of the learning potential.

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