scholarly journals The Complexity Landscape of Outcome Determination in Judgment Aggregation

2020 ◽  
Vol 69 ◽  
Author(s):  
Ulle Endriss ◽  
Ronald De Haan ◽  
Jérôme Lang ◽  
Marija Slavkovik
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Legal Theory ◽  
Expressive Power ◽  
Comprehensive Analysis ◽  
Judgment Aggregation ◽  
Collective Decision Making ◽  
Collective Judgment ◽  
Flexible Framework ◽  
New Framework

We provide a comprehensive analysis of the computational complexity of the outcome determination problem for the most important aggregation rules proposed in the literature on logic-based judgment aggregation. Judgment aggregation is a powerful and flexible framework for studying problems of collective decision making that has attracted interest in a range of disciplines, including Legal Theory, Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing the outcome for a given list of individual judgments to be aggregated into a single collective judgment is the most fundamental algorithmic challenge arising in this context. Our analysis applies to several different variants of the basic framework of judgment aggregation that have been discussed in the literature, as well as to a new framework that encompasses all existing such frameworks in terms of expressive power and representational succinctness.

Corruption and Anticorruption in Democratic Athens

2017 ◽  
Author(s):  
Claire Taylor
Keyword(s):  
Decision Making ◽  
Participatory Democracy ◽  
Collective Decision ◽  
Civic Culture ◽  
Collective Decision Making ◽  
Alexander The Great ◽  
Political Equality ◽  
Individual Judgment ◽  
Collective Judgment ◽  
Hidden Knowledge

The chapter examines a major corruption scandal that involved the Athenian orator Demosthenes and an official of Alexander the Great. This episode reveals how tensions between individual and collective decision-making practices shaped Athenian understandings of corruption and anticorruption. The various and multiple anticorruption measures of Athens sought to bring ‘hidden’ knowledge into the open and thereby remove information from the realm of individual judgment, placing it instead into the realm of collective judgment. The Athenian experience therefore suggests that participatory democracy, and a civic culture that fosters political equality rather than reliance on individual expertise, provides a key bulwark against corruption.

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.

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.

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

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.

Engineering Decision Making in an Open Age

2013 ◽  
Author(s):  
Shuichi Fukuda
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Dynamic Decision Making ◽  
Trial And Error ◽  
Lazy Evaluation ◽  
Team Members ◽  
New Framework ◽  
Previous Step

In an age of diversification and changes, a new framework for decision making for a team is required. As growing complexity and diversification call for team members from a wide variety of areas, a decision cannot be made one-time as it used to be and it must be reached by trials and errors step by step. Such dynamic decision making has to convince members at each step by providing different perspectives for each member to understand the line of reasoning, and must allow lazy evaluation, because some members cannot understand what pieces of knowledge and experience are called for until later step, when clearer perspective is available. Steps proceed by satisfying at least one member. If it fails, then it backtracks to the previous step until it satisfies one more member. This process is repeated until all members are satisfied. Artificial Intelligence allows such trial and error decision making to make all members feel satisfied. The usefulness of this approach is demonstrated by applying developed WPS producing tool to many applications in industry. And it is believed this DDM tool will be very useful for decision making in other areas, too, where systems are very complex and diverse.

scholarly journals Strategic manipulation in judgment aggregation under higher-level reasoning

2021 ◽  
Author(s):  
Zoi Terzopoulou ◽  
Ulle Endriss
Keyword(s):  
Decision Making ◽  
Natural Number ◽  
Partial Information ◽  
Formal Model ◽  
Judgment Aggregation ◽  
Collective Decision ◽  
Aggregation Rule ◽  
Collective Decision Making ◽  
Strategic Behaviour ◽  
Strategic Manipulation

AbstractWe analyse the incentives of individuals to misrepresent their truthful judgments when engaged in collective decision-making. Our focus is on scenarios in which individuals reason about the incentives of others before choosing which judgments to report themselves. To this end, we introduce a formal model of strategic behaviour in logic-based judgment aggregation that accounts for such higher-level reasoning as well as the fact that individuals may only have partial information about the truthful judgments and preferences of their peers. We find that every aggregation rule must belong to exactly one of three possible categories: it is either (i) immune to strategic manipulation for every level of reasoning, or (ii) manipulable for every level of reasoning, or (iii) immune to manipulation only for every kth level of reasoning, for some natural number k greater than 1.

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