Advertisement

Modeling factions for ‘effects based operations’, part II: behavioral game theory

  • Barry G. Silverman
  • Gnana Bharathy
  • Benjamin Nye
  • Tony Smith
Article

Abstract

Military, diplomatic, and intelligence analysts are increasingly interested in having a valid system of models that span the social sciences and interoperate so that one can determine the effects that may arise from alternative operations (courses of action) in different lands. Part I of this article concentrated on internal validity of the components of such a synthetic framework—a world diplomacy game as well as the agent architecture for modeling leaders and followers in different conflicts. But how valid are such model collections once they are integrated together and used out-of-sample (see Sect. 1)? Section 2 compares these realistic, descriptive agents to normative rational actor theory and offers equilibria insights for conflict games. Sections 3 and 4 offer two real world cases (Iraq and SE Asia) where the agent models are subjected to validity tests and an effects based operations (EBO, as in Smith, Effects based operations: applying network-centric warfare in peace, crisis, and war, 2002) experiment is then run for each case. We conclude by arguing that substantial effort on game realism, best-of-breed social science models, and agent validation efforts is essential if analytic experiments are to effectively explore conflicts and alternative ways to influence outcomes. Such efforts are likely to improve behavioral game theory as well.

Keywords

Political simulation Agent-based models Game theory Validation Policy analysis tools 

Abbreviations

S2

Pertains to dyadic scenarios, can be considered a simplified subgame in a triadic interaction. Dyadic scenarios are described without S2 prefix

S3

Pertains to triadic scenarios

S3.1,S3.2,…,S3.6

Each one is a triadic scenario

S2x[FxFy]

Payoff to x in a dyadic scenario, when Both x and y are fighting. Mutual conflict

S2x[FxCy]

Payoff to x in a dyadic scenario, when x is fighting while y has compromised

S2x[CxFy]

Payoff to x in a dyadic scenario, when y is fighting while x has compromised

S2x[CxCy]

Payoff to x in a dyadic scenario, when both x and y have compromised. Mutual compromise

S3x[FxFy,FxFz,FyFz]

Payoff to x in a triadic scenario, when x, y and z are fighting with each other. Mutual conflict

S3x[CxFy,CxFz,CyCz]

Payoff to x in a triadic scenario, when the aggressors y and z independently attack a passive x

S3x[CxCy,CxFz,CyFz]

Payoff to x in a triadic scenario, when z attacks coalition of x and y, who do not fight back

S3x[CxCy,FxFz,FyFz]

Payoff to x in a triadic scenario, when z is fighting with coalition of x and y

S3x[CxCy,CxCz,CyCz]

Payoff to x in a triadic scenario, when there is mutual cooperation/ compromise

i

Discount rate discounting future payoffs to account for time value of payoffs

X, Y, Z

Leaders in the world. Also used as x, y, z when subscripted

Q(D)

Level of attack D=j

Q(Dzx)

Level of attack that denotes the attack is by leader Z on leader X

Q(Dz_xy)

Level of attack where the attack is by leader Z on the coalition of leader X and Y

Q(DZY_X)

Level of attack which denotes that the attack is by the coalition of leaders Z and Y on leader X

Rx, Ry, Rz

Total resources of X, Y, Z

R2

The total resources in a dyadic interaction Rx+Ry=R2

R3

The total resources in triadic interaction be Rx+Ry+Rz=R3

Rdy

Disputed or contested Resource share that belongs to Leader y when both x and y are compromising

Rdx

Disputed or contested Resource share that belongs to Leader x when both x and y are compromising

Rd

Total pool Disputed or contested Resource that will be shared by the Leaders, when both x and y are compromising

ΔKxy(Fx,Fy)

Changed in dyadic relationships between x and y. This is a function of relationships between the leaders as well as the actions taken. This could also be described as ΔKxy(Dxy,Dyx)

CstB (Dxy)

The cost of staging a battle in a dyadic interaction (x launching a battle against y)

Px

Probability of winning in a battle, and is proportional to level (effort) of attack (Q(D yx )) and relative strength Ry/(Rx+Ry) of the attacker Px=(Q(D yx )). Ry/(Rx+Ry)

Q(Dyx)(Ry/(Rx+Ry))Rdx

The expected loss in a given battle for a target is proportional to the level of attack, likelihood of success and the level of resource contested. This is const.(relative strength of attacker)(contested resource of attacked)

Q(Dzx)(Rz/R3)Rdx

Expected losses to x due to being attacked by z using relative resources available (Rz/R3). The attack takes place on the contested resource Rdx, which belongs to x

emV (Fx,Cy)

Emotional payoff (non-material utility) for X from X fighting while Y compromising

emV Tz(Fx,Cy)

Transitive emotion for z, due to the interaction of x, y and z

S_._x(t)

Refers to the payoff for x in scenario S _._ occurring in time step t

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Armstrong JS (2002) Assessing game theory, role playing and unaided judgment. Int J Forecast 18:345–352 CrossRefGoogle Scholar
  2. Axelrod R, Bennett S (1993) A landscape theory of aggregation, Br J Political Sci Google Scholar
  3. Bharathy GK (2006) Agent based human behavior modeling: a knowledge engineering based systems methodology for integrating of social science frameworks for modeling agents with cognition, personality & culture. Doctoral dissertation, University of Pennsylvania Google Scholar
  4. Camerer C (2003) Behavioral game theory. Princeton University Press, Princeton Google Scholar
  5. Collier P, Hoeffler A (2001) Greed and grievance in civil war. World Bank, Washington. Available at www.worldbank.org/research/conflict/papers/greedandgrievance.htm Google Scholar
  6. Dutta PK (2000) Strategies and games: theory and practice. MIT Press, Cambridge Google Scholar
  7. Epstein J, Steinbruner JD, Parker MT (2001) Modeling civil violence: an agent-based computational approach. In: Proceedings of the national academy of sciences. Brookings, Washington Google Scholar
  8. Evans A (2006) Understanding madrasahs. Foreign Affairs, v.85, n.1, Jan/Feb Google Scholar
  9. Giocoli N (2003) Modeling rational agents: from interwar economics to early modern game theory. Elgar Publishing, London Google Scholar
  10. Green KC (2002) Forecasting decisions in conflict situations: a comparison of game theory, role playing and unaided judgment. Int J Forecast 18:321–344 CrossRefGoogle Scholar
  11. Heuer RJ Jr (1999) Psychology of intelligence analysis. Center for the Study of Intelligence, Central Intelligence Agency, Washington Google Scholar
  12. Hirschman AO (1970) Exit, voice, and loyalty. Harvard University Press, Cambridge Google Scholar
  13. Kaneko M (1982). Some remarks on the folk theorem in game theory. Math Soc Sci 3(3) Google Scholar
  14. McCrabb MJ, Caroli JA (2002) Behavioral modeling and wargaming for effects-based operations. In: Proceedings of the military operations research society annual meeting. MORS, Washington Google Scholar
  15. Macy MW, Flache A (2002) Learning dynamics in social dilemmas. Proc Natl Acad Sci 99(3):7229–7236 CrossRefGoogle Scholar
  16. Parks CD, Rumble AC (2001) Elements of reciprocity and social value orientation. Pers Soc Psychol 27(10):1301–1309 CrossRefGoogle Scholar
  17. Pruitt DG, Kimmel MJ (1977) Twenty years of experimental gaming: critique, synthesis and suggestions for the future. Annu Rev Psychol 28:363–392. CrossRefGoogle Scholar
  18. Sageman M (2005) Understanding terror networks. University of Pennsylvania Press, Philadelphia Google Scholar
  19. Silverman BG, Bharathy G (2005) Modeling the personality & cognition of leaders. In: 14th conference on behavioral representations in modeling and simulation, SISO, May 2005. www.sisostds.org
  20. Silverman BG, Rees R et al. (2005) Athena’s prism: a diplomatic strategy role playing game for generating ideas and exploring alternatives. In: Proceedings of the international conference on intelligence analysis. Mitre, MacLean Google Scholar
  21. Silverman BG, Johns M, Cornwell J, O’Brien K (2006a) Human behavior models for agents in simulators and games, part I: enabling science with PMFserv. Presence 15(2) Google Scholar
  22. Silverman BG, O’Brien K, Cornwell J (2006b). Human behavior models for agents in simulators and games: part II: gamebot engineering with PMFserv. Presence 15(2) Google Scholar
  23. Silverman BG, Bharathy G, Nye B (2007a) Gaming and simulating ethnopolitical conflicts. In: Proceedings of the Descartes conference on mathematical modeling for counter-terrorism (DCMMC). Springer, New York Google Scholar
  24. Silverman BG, Bharathy GK, Johns, et al. (2007b) Socio-cultural games for training and analysis (submitted for publication). Available at: www.seas.upenn.edu/~barryg/CultureGames.pdf
  25. Smith EA Jr (2002) Effects based operations: applying network-centric warfare in peace, crisis, and war. Department of Defense Command and Control Research Program, Washington Google Scholar
  26. Wood EJ (2003) Distributional settlements and civil war resolution: stakes, expectations, and optimal agreements. J Confl Resol (under revision for re-submission) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Barry G. Silverman
    • 1
  • Gnana Bharathy
    • 1
  • Benjamin Nye
    • 1
  • Tony Smith
    • 1
  1. 1.Electrical and Systems Engineering Dept.University of PennsylvaniaPhiladelphiaUSA

Personalised recommendations