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SISTER: A symbolic interactionist s...

Duong, Deborah Vakas.

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SISTER: A symbolic interactionist simulation of trade and emergent roles.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	SISTER: A symbolic interactionist simulation of trade and emergent roles./
作者:	Duong, Deborah Vakas.
面頁冊數:	112 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-03, Section: B, page: 1398.
Contained By:	Dissertation Abstracts International65-03B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3126862
ISBN:	0496741403

SISTER: A symbolic interactionist simulation of trade and emergent roles.
Duong, Deborah Vakas.

SISTER: A symbolic interactionist simulation of trade and emergent roles. - 112 p.

Source: Dissertation Abstracts International, Volume: 65-03, Section: B, page: 1398.

Thesis (Ph.D.)--George Mason University, 2004.

SISTER, a Symbolic Interactionist Simulation of Trade and Emergent Roles, captures the fundamental social process by which macro level roles emerge from micro level symbolic interaction. SISTER may be used for the computational social science objective of modeling social coordination in human societies, or for the artificial intelligence objective of coordinating coevolving agents in artificial societies. The knowledge in a SISTER society is held culturally, suspended in the mutual expectations agents have of each other based on signs (tags) that they read and display. In this study, this knowledge includes how to create complex goods. The knowledge of coordinating their creation arises endogenously. A symbol system emerges to denote these tasks. In terms of information theory, the degree of mutual information between the agent's signs (tags) and their behavior increases over time. In SISTER societies, mutual information can grow endogenously and be maintained robustly, even when agents die and are replaced, or are spread out over space.

ISBN: 0496741403Subjects--Topical Terms:

626642
Computer Science.

SISTER: A symbolic interactionist simulation of trade and emergent roles.
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500 $a Director: John Grefenstette.
502 $a Thesis (Ph.D.)--George Mason University, 2004.
520 $a SISTER, a Symbolic Interactionist Simulation of Trade and Emergent Roles, captures the fundamental social process by which macro level roles emerge from micro level symbolic interaction. SISTER may be used for the computational social science objective of modeling social coordination in human societies, or for the artificial intelligence objective of coordinating coevolving agents in artificial societies. The knowledge in a SISTER society is held culturally, suspended in the mutual expectations agents have of each other based on signs (tags) that they read and display. In this study, this knowledge includes how to create complex goods. The knowledge of coordinating their creation arises endogenously. A symbol system emerges to denote these tasks. In terms of information theory, the degree of mutual information between the agent's signs (tags) and their behavior increases over time. In SISTER societies, mutual information can grow endogenously and be maintained robustly, even when agents die and are replaced, or are spread out over space.
520 $a The SISTER society of this study is an economic simulation, in which agents have the choice of growing all the goods they need by themselves, or concentrating their efforts in making more of fewer goods and trading them for other goods. They induce the sign of an agent to trade with, while at the same time, they induce a sign to display. The signs come to mean sets of behaviors, or roles, through this double induction. A system of roles emerges, holding the knowledge of social coordination needed to distribute tasks amongst the agents. This knowledge is maintained despite the stresses of the death and dislocation of individual agents.
520 $a This dissertation contributes to artificial intelligence by demonstrating how agents may divide their labor in a task endogenously. It shows how a complex endogenous communication system can develop to coordinate a complex division of tasking. It also shows that with a SISTER algorithm in which agents die, coevolving systems can avoid convergence and continue to grow in knowledge. With the SISTER algorithm, robot societies can robustly continue to maintain their symbols and roles despite death and distance.
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