Design DiffServ Multicast with Selfish Agents

2005 ◽  
pp. 214-223
Author(s):  
WeiZhao Wang ◽  
Xiang-Yang Li ◽  
Zheng Sun
Keyword(s):  
Selfish Agents

scholarly journals HOW SHOULD WE RECONCILE SELF-REGARDING AND PRO-SOCIAL MOTIVATIONS? A RENAISSANCE OF “DAS ADAM SMITH PROBLEM”

2020 ◽  
Vol 37 (1) ◽  
pp. 80-102
Author(s):  
Natalie Gold
Keyword(s):  
Adam Smith ◽  
Crowding Out ◽  
Private Interests ◽  
Self Interest ◽  
Intrinsic Motivations ◽  
Social Motivations ◽  
Motivation Crowding ◽  
Wealth Of Nations ◽  
Selfish Agents

Abstract“Das Adam Smith Problem” is the name given by eighteenth-century German scholars to the question of how to reconcile the role of self-interest in the Wealth of Nations with Smith’s advocacy of sympathy in Theory of Moral Sentiments. As the discipline of economics developed, it focused on the interaction of selfish agents, pursuing their private interests. However, behavioral economists have rediscovered the existence and importance of multiple motivations, and a new Das Adam Smith Problem has arisen, of how to accommodate self-regarding and pro-social motivations in a single system. This question is particularly important because of evidence of motivation crowding, where paying people can backfire, with payments achieving the opposite effects of those intended. Psychologists have proposed a mechanism for the crowding out of “intrinsic motivations” for doing a task, when payment is used to incentivize effort. However, they argue that pro-social motivations are different from these intrinsic motivations, implying that crowding out of pro-social motivations requires a different mechanism. In this essay I present an answer to the new Das Adam Smith problem, proposing a mechanism that can underpin the crowding out of both pro-social and intrinsic motivations, whereby motivations are prompted by frames and motivation crowding is underpinned by the crowding out of frames. I explore some of the implications of this mechanism for research and policy.

scholarly journals Improvements for Truthful Mechanisms with Verifiable One-Parameter Selfish Agents

2006 ◽  
pp. 147-160 ◽  
Author(s):  
A. Ferrante ◽  
G. Parlato ◽  
F. Sorrentino ◽  
C. Ventre
Keyword(s):  
Selfish Agents

scholarly journals Maximise Global Gain in the Minority Game

2009 ◽  
Vol 2009 ◽  
pp. 1-4 ◽  
Author(s):  
Sy-Sang Liaw
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Minority Game ◽  
Traffic System ◽  
Selfish Agents

We find a simple, partially altruistic mechanism that can increase global gain for a community of selfish agents. The mechanism is implied in the phenomena found in the minority game. We apply the mechanism to a two-road traffic system to maximise traffic flow.

The build-up of cooperative behavior among non-cooperative selfish agents

2008 ◽  
Vol 67 (1) ◽  
pp. 239-252 ◽  
Author(s):  
Hassan Benchekroun ◽  
Ngo Van Long
Keyword(s):  
Cooperative Behavior ◽  
Selfish Agents

scholarly journals Robust Norm Emergence by Revealing and Reasoning about Context: Socially Intelligent Agents for Enhancing Privacy

2018 ◽  
Author(s):  
Nirav Ajmeri ◽  
Hui Guo ◽  
Pradeep K. Murukannaiah ◽  
Munindar P. Singh
Keyword(s):  
Social Cohesion ◽  
Intelligent Agents ◽  
Specific Context ◽  
The Social ◽  
Social Architecture ◽  
Selfish Agents ◽  
Norm Emergence

Norms describe the social architecture of a society and govern the interactions of its member agents. It may be appropriate for an agent to deviate from a norm; the deviation being indicative of a specialized norm applying under a specific context. Existing approaches for norm emergence assume simplified interactions wherein deviations are negatively sanctioned. We investigate via simulation the benefits of enriched interactions where deviating agents share selected elements of their contexts. We find that as a result (1) the norms are learned better with fewer sanctions, indicating improved social cohesion; and (2) the agents are better able to satisfy their individual goals. These results are robust under societies of varying sizes and characteristics reflecting pragmatic, considerate, and selfish agents.

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