Title :
Representing Diversity in Communities of Bayesian Decision-makers
Author :
Greene, Kshanti A. ; Kniss, Joe M. ; Luger, George F.
Author_Institution :
Dept. of Comput. Sci., Univ. of New Mexico, Albuquerque, NM, USA
Abstract :
High-quality information has emerged from the contributions of many using the wiki paradigm. A logical next step is to use the wisdom of the crowd philosophy to solve complex problems and produce informed policy. We introduce a new approach to aggregating the beliefs and preferences of many individuals to form models that can be used in social policy and decision-making. Traditional social choice functions used to aggregate beliefs and preferences attempt to find a single solution for the whole population, but may produce an irrational social choice when a stalemate between opposing objectives occurs. Our approach, called collective belief aggregation, partitions a population into collectives that share a preference order over the expected utilities of decision options or the posterior likelihoods of a probabilistic variable. It can be shown that if a group of individuals share a preference order over the options, their aggregate will uphold principles of rational aggregation defined by social choice theorists. Super-agents can then be formed for each collective that accurately represent the preferences of their collective. These super-agents can be used to represent the collectives in decision analysis and decision-making tasks. We demonstrate the potential of using collective belief aggregation to incorporate the objectives of stakeholders in policy-making using preferences elicited from people about healthcare policy.
Keywords :
belief networks; decision making; groupware; multi-agent systems; probability; social sciences computing; Bayesian community; collective belief aggregation; decision analysis; decision making; posterior likelihood; social policy; super-agents; Aggregates; Bayesian methods; Biological system modeling; Computational modeling; Decision making; Inference algorithms; Medical services; Bayesian models; belief aggregation; decision-making; game theory; negotiation; preference aggregation; social choice;
Conference_Titel :
Social Computing (SocialCom), 2010 IEEE Second International Conference on
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4244-8439-3
Electronic_ISBN :
978-0-7695-4211-9
DOI :
10.1109/SocialCom.2010.52