By Gennaro Di Tosto, Frank Dignum (auth.), Francesca Giardini, Frédéric Amblard (eds.)

ISBN-10: 3642388582

ISBN-13: 9783642388583

ISBN-10: 3642388590

ISBN-13: 9783642388590

This ebook constitutes the completely refereed post-conference complaints of the thirteenth foreign Workshop on Multi-Agent-Based Simulation, MABS 2012, held in Valencia, Spain, in June 2012. The eleven revised complete papers provided have been rigorously chosen from 35 submissions. The papers are geared up in topical sections on modeling social interactions; cognition and brokers behaviors; brokers, video games and finance; and methodologies and tools.

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Extra info for Multi-Agent-Based Simulation XIII: International Workshop, MABS 2012, Valencia, Spain, June 4-8, 2012, Revised Selected Papers

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Beyond that, our decision-makers suffer from biases which we regard to be the distinctive feature of our model. In prior research human biases have been modeled and implemented in several ways like in [16] who analyses the anchoring effect of consumer price negotiations within the bargaining-zone model (the bargaining zone is the difference Effects of Combined Human Decision-Making Biases 29 between the buyer’s and seller’s reservation prices). Kant and Thiriot [17] describe a cognitive decision agent model, where the decision-makers are modeled in a multi-agent system.

Recency & status quo, self-contained, departmental incentives 34 S. Berlinger and F. , α = 0 and q = 0 (see Table 3), organizations achieve a high level of performance if departmental incentives are given. By intensifying the status quo bias the final performance decreases. However, the effects of the status quo bias on the final performance are apparently compensated by the recency effect (see Figure 1). Even for high levels of the status quo bias, the final performance increases continuously to a level achieved without any biases if the recency effect is more intense.

4d to reject the offer publicly. The client C1 has no prior knowledge of c seller’s deception till time step t3 but after time step t7 this Bdec is not taken w into consideration by other client’s Bdec . In Experiment 3, C1, C2 and C3 are not aware of the deception, having c w Bdec = 0 and Bdec = 0 until t=15 when the seller is asking C1 for money. Although C1 had witnessed the interaction of the seller with other clients, he had not seen any evidence of deception. Without having the support of the crowd in marking the seller as deceptive, C1 has no argument to reject the payment asked by seller.

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Multi-Agent-Based Simulation XIII: International Workshop, MABS 2012, Valencia, Spain, June 4-8, 2012, Revised Selected Papers by Gennaro Di Tosto, Frank Dignum (auth.), Francesca Giardini, Frédéric Amblard (eds.)


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