scholarly journals Borda Count in Collective Decision Making: A Summary of Recent Results

2019 ◽  
Vol 33 ◽  
pp. 9830-9836
Author(s):  
Jörg Rothe
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Strategic Behavior ◽  
Collective Decision ◽  
Borda Count ◽  
Collective Decision Making ◽  
Computational Social Choice ◽  
Indivisible Goods ◽  
Voting Rules ◽  
Ordinal Preferences

Borda Count is one of the earliest and most important voting rules. Going far beyond voting, we summarize recent advances related to Borda in computational social choice and, more generally, in collective decision making. We first present a variety of well known attacks modeling strategic behavior in voting—including manipulation, control, and bribery—and discuss how resistant Borda is to them in terms of computational complexity. We then describe how Borda can be used to maximize social welfare when indivisible goods are to be allocated to agents with ordinal preferences. Finally, we illustrate the use of Borda in forming coalitions of players in a certain type of hedonic game. All these approaches are central to applications in artificial intelligence.

scholarly journals Towards Trust, Transparency and Liability in AI / AS systems

2017 ◽  
Author(s):  
Eva Thelisson
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Autonomous Systems ◽  
Research Problem ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Global Level ◽  
Governance Mechanisms ◽  
Decision Making Processes ◽  
Artificial Intelligence Systems

The research problem being investigated in this article is how to develop governance mechanisms and collective decision-making processes at a global level for Artificial Intelligence systems (AI) and Autonomous systems (AS), which would enhance confidence in AI and AS.

The Elements of Collective Decision Making

2012 ◽  
pp. 1-31
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Group Decision Making ◽  
Real World ◽  
Group Decision ◽  
Collaborative Work ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Decision Science ◽  
Computer Supported Collaborative Work

In this chapter, the concept of a reasoning community is introduced. The overarching motivation is to understand reasoning within groups in real world settings so that technologies can be designed to better support the process. Four phases of the process of reasoning by a community are discerned: engagement of participants, individual reasoning, group coalescing, and, ultimately, group decision making. A reasoning community is contrasted with communities of practice and juxtaposed against concepts in related endeavours including computer supported collaborative work, decision science, and artificial intelligence.

Nonmanipulable Collective Decision-Making for Games

2012 ◽  
pp. 67-81
Author(s):  
Rob LeGrand ◽  
Timothy Roden ◽  
Ron K. Cytron
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Collective Decision ◽  
The Other ◽  
Collective Decision Making ◽  
Game Development ◽  
New Approach ◽  
Collective Decisions ◽  
Higher Dimensional ◽  
Game Players

This chapter explores a new approach that may be used in game development to help human players and/or non-player characters make collective decisions. The chapter describes how previous work can be applied to allow game players to form a consensus from a simple range of possible outcomes in such a way that no player can manipulate it at the expense of the other players. Then, the text extends that result and shows how nonmanipulable consensus can be found in higher-dimensional outcome spaces. The results may be useful when developing artificial intelligence for non-player characters or constructing frameworks to aid cooperation among human players.

scholarly journals Improving human collective decision-making through animal and artificial intelligence

2021 ◽  
Vol 1 ◽  
Author(s):  
Cédric Sueur ◽  
Christophe Bousquet ◽  
Romain Espinosa ◽  
Jean-Louis Deneubourg
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Collective Decision ◽  
Collective Decision Making

scholarly journals Collective Decision Making under Incomplete Knowledge: Possible and Necessary Solutions

2020 ◽  
Author(s):  
Jérôme Lang
Keyword(s):  
Decision Making ◽  
Coalition Formation ◽  
Fair Division ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Voting Rules ◽  
Incomplete Knowledge ◽  
Incomplete Preference ◽  
Complete Knowledge ◽  
Stability Concepts

Most solution concepts in collective decision making are defined assuming complete knowledge of individuals' preferences and of the mechanism used for aggregating them. This is often unpractical or unrealistic. Under incomplete knowledge, a solution advocated by many consists in quanrtifying over all completions of the incomplete preference profile (or all instantiations of the incompletely specified mechanism). Voting rules can be `modalized' this way (leading to the notions of possible and necessary winners), and also efficiency and fairness notions in fair division, stability concepts in coalition formation, and more. I give here a survey of works along this line.

Accelerating AI

2012 ◽  
Author(s):  
John O. McGinnis
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
General Purpose ◽  
Machine Intelligence ◽  
Collective Decision ◽  
Government Support ◽  
Collective Decision Making ◽  
Process Data ◽  
The World ◽  
Future Policy

This chapter focuses on artificial intelligence (AI). The development of machine intelligence can directly improve governance, because progress in AI can help in assessing policy consequences. More substantial machine intelligence can process data, generate hypotheses about the effects of past policy, and simulate the world to predict the effects of future policy. Thus, it is more important to formulate a correct policy toward AI than toward any other rapidly advancing technology, because that policy will help advance beneficial policies in all other areas. The holy grail of AI is so-called strong AI, defined as a general purpose intelligence that approximates that of humans. The correct policy for AI—substantial government support for Friendly AI—both promotes AI as an instrument of collective decision making and helps prevent the risk of machine takeover.

scholarly journals Preference Handling in Combinatorial Domains: From AI to Social Choice

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 37 ◽  
Author(s):  
Yann Chevaleyre ◽  
Ulle Endriss ◽  
Jérôme Lang ◽  
Nicolas Maudet
Keyword(s):  
Decision Making ◽  
Social Choice ◽  
Social Choice Theory ◽  
Collective Decision ◽  
Compact Representation ◽  
Collective Decision Making ◽  
Computational Social Choice ◽  
Computational Point ◽  
Multiagent Resource Allocation ◽  
Preference Handling

In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multi-attribute) structure. We give an introduction to preference handling in combinatorial domains in the context of collective decision making, and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains, and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, known as computational social choice, that brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

Sign in / Sign up

Export Citation Format

Share Document