Advances in Social Computing: Third International Conference by Stephen Eubank, Anil Vullikanti, Maleq Khan, Madhav Marathe

By Stephen Eubank, Anil Vullikanti, Maleq Khan, Madhav Marathe (auth.), Sun-Ki Chai, John J. Salerno, Patricia L. Mabry (eds.)

Social computing is anxious with the examine of social habit and social context in response to computational structures. Behavioral modeling presents a illustration of the social habit, and allows experimenting, situation making plans, and deep und- status of habit, styles, and capability results. The pervasive use of machine and web applied sciences by means of people in way of life presents an unparalleled en- ronment of varied social actions that, as a result of the systems below which they occur, generate quite a lot of kept info as a derivative, frequently in systematically prepared shape. Social computing enables behavioral modeling in version development, research, development mining, and prediction. a variety of interdisciplinary and interdepe- ent platforms are created and used to symbolize a few of the social and actual platforms for investigating the interactions among teams, groups, or geographical regions. This calls for joint efforts to exploit the cutting-edge examine from a number of disciplines bettering social computing and behavioral modeling with the intention to rfile classes realized and improve novel theories, experiments, and methodologies to higher clarify the interplay among social (both casual and institutionalized), psyc- logical, and actual mechanisms. The objective is to permit us to test, create, and recreate an operational atmosphere with a greater figuring out of the contributions from every one person self-discipline, forging joint interdisciplinary efforts. This quantity contains the court cases of the 3rd overseas workshop on - cial Computing, Behavioral Modeling and Prediction, which has grown trem- dously.

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Extra info for Advances in Social Computing: Third International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010, Bethesda, MD, USA, March 30-31, 2010. Proceedings

Example text

In each generation, each agent must make a sequence of n choices, where each choice is between two lotteries with equal expected value but different risks. One lottery has a certain outcome of payoff 4 (with probability 1), we call this the safe lottery. 5, we call this the risky lottery. Both lotteries have an expected value of 4, the only difference is the payoff distribution. Within this class, we can define different games by varying two important game features, both of which are discussed below: the number n of choices in the sequence, and the reproduction dynamics.

When Imitation was chosen, the persistent identifier of the copied player was recorded to allow further analyses of imitation decisions. In the case of a player replacing the entire team with Imitated icons, only the choices that were not already present on the team were counted as Imitated. Similar criteria applied to replacement of an entire team with Retrieved icons, or removing an icon and then putting it back on the team via an Innovation choice. Choice similarity was defined as the proportion of icons that two teams have in common.

Groups of participants play simultaneously, and each can view the tentative solutions of all others. In one condition, participants may view fellow participants’ scores alongside their solutions, and in another condition fellow participants’ scores are invisible. N. L. 4 Predictions We made the following predictions. When evaluative information about peer solutions was unavailable, participants would be unable to be sufficiently selective in imitation, and thus participants employing highly imitative strategies would have relatively lower scores.

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