Shah Jamal Alam, Frank Hillebrandt and Michael Schillo (2005)
Sociological Implications of Gift Exchange in Multiagent Systems
Journal of Artificial Societies and Social Simulation
vol. 8, no. 3
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Received: 23-Aug-2004 Accepted: 04-Apr-2005 Published: 30-Jun-2005
The most basic difference (between social and strictly economic exchange) is that the obligations incurred in social transactions are not clearly specified in advance. [...] In social exchange, by contrast, one party supplies benefits to another, and although there is a general expectation of reciprocation, the exact nature of the return is left unspecified. Indeed, it must remain unspecified, since any attempt to specify it in advance destroys the social meaning of the transaction by transforming it into a merely economic one. Doing a favor has an entirely different social significance from making a bargain. (Blau 1974)The aim of gift exchange is to build stronger relationship, which may be beneficial in the long-run. As Schillo (2004) explains,
the results of gift-giving are increased social bonds that may not be (economically) rational but help to stabilise social systems in otherwise brittle environments, e.g. in the absence of formal rules. In economic exchange, a good or task is exchanged for money, assuming that the value of both is of appropriate similarity. Gift exchange, however, is the negation of this concept: the mutually deliberate deviation from the economic exchange. The motivation for the gift exchange is the expectation of either reciprocation, or the refusal of reciprocation.
In symbolic exchanges like gift exchange time plays a decisive role. The inaugural act that institutes communication (by addressing words, offering a gift, issuing an invitation or a challenge, etc.) always entails a kind of intrusion or even a calling into question ... In addition, it always inevitably contains the potentiality of a hold, a bond. It is true, contrary to what the structuralists' mechanical model would suggest, it implies uncertainty and therefore a temporal opening: one can always choose not to reply to the interpellation, invitation or challenge or not to reply immediately, to defer and to leave the other party in expectation. (Bourdieu 2000).
|Figure 1. The delegation matrix with Task Delegation and Social Delegation|
|Figure 2. Example of a configuration where two different groups of customers are introduced|
|Figure 3. Example of a configuration where three different types of providers are introduced|
|Figure 4. General settings for the system|
Counter-Gift Receiver Strategy: A GE agent in our model is conscious of reciprocating a gift that it had received from another provider in the past, especially if it had not sent any counter-gift at all. Having such a provider to whom it is indebted, participating in its auction, provides the chance to reciprocate a gift and therefore proving itself as a gift-giving agent. Care is taken that the value of the gift should be different and that 'some' time is passed since the gift was received. Since the CNCP auction is an opportunity to offer gifts to providers in order to know their types, this strategy is not selected always, and the following strategy is chosen:
Best-Bidder Strategy: Gives the chance of picking among the committed participants, the provider with the highest bid. If not, any of the bidders is selected uniform randomly.
|Figure 5. A GE provider's perspective of other providers in the market|
|Figure 6. Scheme for sending gift via Weak Gift Exchange|
|Figure 7. Interaction of agents using Weak Gift Exchange shown using UML Activity Diagrams|
|Figure 8. Counter-gift via strong gift exchange|
|Figure 9. An illustration of triggering strong gift exchange|
Hypothesis I: In scenarios where the message-limit (number of call for proposals sent by an auctioneer) and the market size are low, the number of gift exchanges among the gift-giving agents is reduced.
|Figure 10. Series plot showing average number of gifts for the three cases with varying message-limit|
Hypothesis II: We present the hypotheses in two parts, as they may appear correlated to the reader.
II- A: In homogenous market scenarios where all agents are gift-giving, there is no long-term distinct grouping of the agents.
|Figure 11. Number of strongly connected components from buddy network based on three different criteria for buddy selection|
II- B: In heterogeneous market scenarios, introduction of gift exchange partitions the provider agents into two groups over a long run.
|Figure 12. Interactions of GE agents with providers of both types with the GE providers in majority|
|Figure 13. Interactions of GE agents with providers of both types with the GE providers in minority|
When the GE providers were in minority their knowledge about other GE providers increased gradually during the rounds, as shown in the Figure 13. It is understandable that with a lesser number of GE providers in the market, the number of interactions among themselves is expected to remain low. One also witnesses a sharp rise in the second series, showing the increase in the knowledge of GE agents about the EE provider agents.
Hypothesis III: In heterogeneous market scenarios, if the population of GE agents is greater, the EE agents will not be as successful as in the case when the population of GE agents is low. The higher the populations of GE, the higher are the chances of GE agents of being successful.
|Figure 14. A comparison of average profit earned by GE and EE provider agents with varying proportion of GE agents: (a) with high criterion for buddy selection; (b) with moderate criterion|
|Figure 15. A comparison of average wealth earned by the GE providers classified into two groups and EE agents with a high criterion for buddy selection|
|Figure 16. A comparison of average wealth earned by the GE providers classified into two groups and EE agents with a moderate criterion for buddy selection|
Hypothesis IV: If the survival of provider agents with lesser capabilities is difficult, the practice of gift exchange will be intensive. Such GE agents would build buddies in order to gain more capital even though they suffer early losses from the gifts they give.
|Figure 17. Series plots for percentage proportion of less powerful agents versus their contribution to total gifts exchanged, for different initial rounds|
where Contribution (i) is the cost of the ith provider contributing to the task, times the profit. The profit for provider agents can be set in their configuration as shown in Figure 3 and the money earned is profit times the provider's own cost.
|Figure 18. The FIPA definition of the Contract Net Protocol (Schillo 2004)|
|Figure 19. CNCP interaction protocol for Weak GE Mechanism|
|Figure 20. Buddy Inform Protocol|
2The notion of 'information asymmetries' was suggested by one of the anonymous referees. They can be defined as conditions “in which at least some relevant information is known to some but not all parties involved” (InvestorWorld 2005).
BLAU, P. M., 1974, Social Exchange, In: On the Nature of Organizations, John Wiley & Sons, New York, London, Sydney, Toronto, pp. 204-214.
BOURDIEU, P., 1977, Outline of a theory of practice, R. Nice (English Trans.), Princeton University Press, Princeton NJ, USA.
BOURDIEU, P., 2000, Pascalian Meditations, English Trans., Polity Press, Cambridge, UK.
BOURDIEU, P., Wacquant, L., 1992, An Invitation to Reflexive Sociology, Polity Press, Cambridge, UK.
CASTELFRANCHI, C., Conte, R., and Paolucci, M., 1998, Normative reputation and the costs of compliance, Journal of Artificial Societies and Social Simulation, vol. 1, no. 3, http://www.soc.surry.ac.uk/JASSS/1/3/3.html
CARLEY, K., Gasser, L., 1999, Computational Organizational Theory, in Gerhard Weiss (Ed.), Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, The MIT Press, Boston MA, USA.
CORMEN, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.D., 2001, An Introduction to Algorithms, 2nd Edition, The MIT Press, Boston MA, USA.
FERBER, J., 1999, Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, Addison-Wesley, Boston MA.
FIPA, 2003, Foundation for Intelligent Physical Agents, http://www.fipa.org.
FLEY, B., Florian, M., 2004, Trust and the Economy of Symbolic Goods: A Contribution to the Scalability of Open Multi_agent Systems", in Fischer, K., Florian, M., Malsch, T. (Ed.), Socionics: Its Contributions to the Scalability of Complex Social Systems, Springer-Verlag, Berlin et al., (to appear).
GOULDNER, A. W., 1960, The norm of reciprocity: A preliminary statement, American Sociological Review, vol. 25, pp. 161-178.
HAHN, C., Fley, B., Schillo, M., 2003, Strategic Adaptation in Self-organizing Multiagent Systems, in Proceedings of the 4th International Workshop on Modelling Artificial Societies and Hybrid Organizations (MASHO'03), Hamburg 2003.
HILLEBRANDT, F., 2004, Sociological Foundation of the Holonic Approach Using Habitus-Field-Theory to improve Multiagent Systems", in Fischer, K., Florian, M., Malsch, T. (Ed.), Socionics: Its Contributions to the Scalability of Complex Social Systems, Springer-Verlag, Berlin et al., (to appear).
HOGG, L.M.J. and Jennings, N.R., 2001, "Socially Intelligent Reasoning for Autonomous Agents", IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, vol. 31, no. 5, IEEE Press.
INVESTORWORLD, 2005, Investing Glossary, http://www.investorwords.com/2461/information_asymmetry.html
JAFFE, K., 2002, An economic analysis of altruism: who benefits from altruistic acts?, vol. 5, no. 3, Journal of Artificial Societies and Social Simulation, http://jasss.soc.surrey.ac.uk/5/3/3.html
KERBER, W. and Saam, N.J., 2001, Competition as a Test of Hypotheses: Simulation of Knowledge-generating Market Processes, Journal of Artificial Societies and Social Simulation, vol. 4, no. 3, http://www.soc.surrey.ac.uk/jasss/4/3/2.html
KNABE, T., 2002, Business Organizational Forms in Self-organizing Multiagent Systems, Diplomarbeit, Department of Computer Science, Saarland University.
KNABE, T., Schillo, M., Fischer, K., 2002, "Improvements to the FIPA Contact Net Protocol for Performance Increase and Cascading Applications", in Proceedings of the International Workshop for Multi-Agent Interoperability, the 25th German Conference on Artificial Intelligence (KI-2002) Aachen, Springer-Verlag, Berlin et al.
LIND, J., 2002, Specifying Agent Interaction Protocols with Standard UML, in Proceedings of the 2nd International Conference on Autonomous Agents, Lecture Notes in Computer Science, vol. 2222, pp. 136-147, Springer-Verlag, Berlin et al.
MALSCH, T., 2001, Naming the unnamable: Socionics or the sociological turn of/to distributed artificial intelligence, in Proceedings of Autonomous Agents Multi-Agent Systems (AAMAS), pp. 144-186, New York, USA: ACM Press.
MARSH, S., 1994, Formalising Trust as a Computational Concept, PhD Thesis, Department of Computer Science, University of Stirling.
MAUSS, M., 1967, The Gift, I. Cunnison (English Trans.), Norton Press, New York, USA.
ROUCHIER, R., O'Connor, M., and Bousquet, F., 2001, The creation of a reputation in an artificial society by a gift system, Journal of Artificial Societies and Social Simulation, vol. 4, no. 2, http://www.soc.surry.ac.uk/jasss/4/2/8.html
SCHILLO, M., Bürckert, H., Fischer, K., and Klusch, M., 2001, "Towards a Definition of Robustness for Market-Style Open Multi-Agent Systems", in Proceedings of the 5th International Conference on Autonomous Agents (AA'01), New York, USA: ACM Press.
SCHILLO, M., 2002, Self-Organization and Adjustable Autonomy: Two Sides of the Same Medal?, Connection Science, vol. 14, no. 4, Taylor & Francis.
SCHILLO, M., Fley, B., Florian, M., Hillebrandt, F., Hinck, D., 2002, Self-Organization in Multiagent Systems: From Agent Interaction to Agent Organization, in Proceedings of the 3rd International Workshop on Modeling Artificial Societies and Hybrid Organizations (MASHO'02), Workshop at KI2002, the 25th German Conference on Artificial Intelligence Aachen, pp. 47-56.
SCHILLO, M., 2004, Multiagent Robustness: Autonomy vs. Organisation, PhD Thesis, Department of Computer Science, Saarland University.
SCHWARTZ, B., 1967, The Social Psychology of the Gift, American Journal of Sociology, vol. 73, no. 1, pp. 1-11, The University of Chicago Press.
SMITH, R. G., 1980. The Contract Net Protocol: High Level Communication and Control in a Distributed Problem Solver, IEEE Transactions on Computers, vol. 29, no. 12, pp. 1104-1113, IEEE Computer Society Press.
van de VEN, J., 2000, The Economics of the Gift, available at http://cwis.kub.nl/~few5/center/phd stud/ven
WEISS, G., 1999, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, Cambridge, Massachusetts.
WOOLDRIDGE, M., 2002, An Introduction to Multiagent Systems, John Wiley & Sons, New York.
WOOLDRIDGE, M., and Jennings, N.R., 1994, Intelligent agents: Theory and Practice, available at citeseer http://www.citeseer.ist.psu.edu/97055.html
YOUNGER, S., 2004, Reciprocity, Normative Reputation, and the Development of Mutual Obligation in Gift-Giving Societies, Journal of Artificial Societies and Social Simulation, vol. 7, no. 1, http://www.soc.surry.ac.uk/JASSS/7/1/5.html
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