Agents, a broker, and lies

Fankhauser, Péter; Tesch, Thomas

doi:10.1109/ride.1999.758601

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…They will inevitably conflict like the humans they represent [HFH99] and will act as self-interested maximizers without concern to the social global welfare, and thus necessitate the use of automated conflict resolution mechanisms [KF98,SFJ97,Sch00]. Arbitration is one such conflict resolution mechanism [TA98,FT99,TF99,TFO01]. To be efficient and fair, it is desirable to provide the arbiter with the true interests of the parties as early as possible.…”

Section: The Inter-organizational Transaction Flowmentioning

confidence: 99%

Matchmaking software agents in B2B markets

Ouksel¹,

Babad²,

Tesch³

2004

37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of The

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Softwareagents, artificial intelligent "representatives" of the interests of supply chain participants, enhance the effectiveness and efficiency of Business-to-Business (B2B) e-commerce systems that support modern supply chains. The interorganizational transaction flow starts with a matchmaking process in which suppliers' agents are matched with demanders' agents. The matched agents then negotiate either directly or with the aid of an arbiter. The deal agreed to by the negotiating agents is then executed. The matchmaking and arbitration processes support the achievement of transactions that benefits both sides over many repeated transactions. Matchmaking pairs agents with the least conflicting interests, and arbitration seeks consensus between the negotiating parties within reasonable time and costs while discouraging insincere behavior by the participants. Both processes require a trusted, honest intermediary to achieve appropriate matchmaking and arbiter consensus among all the parties. In this paper we analyze matchmaking in a stable two-sided market, where each seller is matched with a single buyer, and all agents on both sides are matched. We discuss matchmaking in conjunction with arbitration protocols, and analyze their performance through simulation.

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Section: The Inter-organizational Transaction Flowmentioning

confidence: 99%

Matchmaking software agents in B2B markets

Ouksel¹,

Babad²,

Tesch³

2004

37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of The

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“…The latter forms the basis of autonomy [15,26,51]. Some systems propose the use of auctioning or the use of a separate agent, i.e., a "broker," to avoid the reasoning costs associated with delegation [21,31,55]. However, we focus on delegation and offer models of trust and autonomy that account for impacts of benevolence, social exchanges, power and norms [16,29,30].…”

Section: Introductionmentioning

confidence: 99%

Delegations guided by trust and autonomy

Hexmoor

Rahimi

Chandran

2008

Web Intelligence and Agent Systems: An International Journal

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This paper explores delegation decisions predicated on models of trust and autonomy among agents. In socially rich environments, trust and autonomy of artificial agents are key attributes for rational delegation decisions. Social agents are affected by many social attributes such as benevolence, social exchanges, power, and norms. We present cognitively inspired working models of trust and autonomy for delegation among agents that accounts for these social attributes. These models are validated in a simulation of a group of agents who delegate tasks in order to locate the most socially well-suited individuals for performing that task. We define autonomy in such an objective manner that agents who have the highest autonomy with respect to a task will generally be the most socially well suited. Benevolence, social reciprocation, and norms most directly contribute to autonomy and proportionally to social suitability. Derived social power directly contributes to social suitability, whereas independently assigned social power ranking overrides the suitability. We show that trust and autonomy in an agent group changes under various social settings.

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“…Yet the last century saw, with the advent of game theory as a means of formalisation and modelling, the paving of a scientific access path to such notions, partially as a side effect of the growing interest in the behaviour of intelligent beings (usually called agents) in social environments [1], which in turn was spurred by the mathematisation of economics. With the emergence of the Internet as a mass medium, this kind of research has obtained a new test bed, a source of statistical data, and an independent study object, and has thus gained further impetus [2,3,4]. Consequently, hard science is lead to occupy itself with formerly foreign concepts from the psychological and sociological domains.…”

Section: Introductionmentioning

confidence: 99%

A model for prejudiced learning in noisy environments

Schmidt

2005

Applied Mathematics and Computation

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Based on the heuristics that maintaining presumptions can be beneficial in uncertain environments, we propose a set of basic requirements for learning systems to incorporate the concept of prejudice. The simplest, memoryless model of a deterministic learning rule obeying the axioms is constructed, and shown to be equivalent to the logistic map. The system's performance is analysed in an environment in which it is subject to external randomness, weighing learning defectiveness against stability gained. The corresponding random dynamical system with inhomogeneous, additive noise is studied, and shown to exhibit the phenomena of noise induced stability and stochastic bifurcations. The overall results allow for the interpretation that prejudice in uncertain environments can entail a considerable portion of stubbornness as a secondary phenomenon.

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Agents, a broker, and lies

Cited by 9 publications

References 15 publications

Matchmaking software agents in B2B markets

Matchmaking software agents in B2B markets

Delegations guided by trust and autonomy

A model for prejudiced learning in noisy environments

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