Matthias Scheutz and Paul Schermerhorn (2004)
The Role of Signaling Action Tendencies in Conflict Resolution
Journal of Artificial Societies and Social Simulation
vol. 7, no. 1
To cite articles published in the Journal of Artificial Societies and Social Simulation, please reference the above information and include paragraph numbers if necessary
Received: 08-Jun-2003 Accepted: 14-Sep-2004 Published: 31-Jan-2004
|Stop||(BS+CS+n×CC, BS+CS+n×CC)||(BS+CS+n×CC, BC+CC+n×CC)|
|CC, CS < 0 < BS < BC.||(1)|
|Figure 1. Decision Mechanism Performance. Lines are paired by color, each pair representing a contest in which the two agents' D-values sum to 1. The probabilities of continuing at 0 iterations are just the original D-values; thereafter the probabilities diverge from one another (except in the (0.5,0.5) case) according to the rule described above|
|Figure 2. The SimWorld artificial life environment (graphical mode); small green circles represent food sources and red circles represent agents, with black tics indicating agents' headings. Here, 0-social agents (labeled "as1_agent" followed by their ID number) are competing against m-social agents (labeled "as2_agent" followed by their ID number)|
;;; the loop to update one simulation cycle foreach cycle do generate new food resources foreach entity in allentities do percepts(entity) := run_sensors(entity, allentities) endforeach foreach entity in allentities do actions(entity) := run_rulesystem(entity, percepts(entity)) endforeach foreach entity in allentities do perform actions(entity) endforeach remove dead agents, update agents' body representations, update counters perform statistics endforeach
;;; select actions based on percepts function run_rulesystem(entity, percepts) returns action; generate a combined force vector for the percepts opponent := detect_opponent(percepts) if exists(opponent) then P := entity.tendency if infinite-social(agent) then if entity.tendency > opponent.display then P := 1 else P := 0 endif else for i from 1 to entity.iterations do if P > opponent.display then P := P + ((1 - P) / P) * (P - opponent.display) else P := P - (P / opponent.display) * (opponent.display - P) endif endfor endif if P ≥ random(1.0) then action := retreat (i.e., generate force away from opponent) else action := fight (i.e., set force to 0) endif else action := move as determined by the force vector endif endfunction
|motion_vector = 20 * ( ∑VF (1 / |VF|2 * VF)) - 20 * ( ∑VS (1 / |VS|2 * VS)) - 5 * ( ∑VO (1 / |VO|2 * VO)) .||(5)|
|Figure 3. Utility of Winning|
|Figure 4. Utility of Losing|
|Figure 5. 0-social vs. m-social|
|Figure 6. m-social vs. ∞-social|
|Figure 7. 0-social Liars vs. m-social|
|Figure 8. 0-social Liars vs. m-social vs. ∞-social|
|Stop||(-90 - 50×n,-90 - 50×n)||(-90 - 50×n,270 - 50×n)|
|Continue||(270 - 50×n,-90 - 50×n)|
and an expected individual utility of
|PCX × (1 - PCY) × 277 + (1 - PCX) × -90 + PCX × PCY × -50 + n × -50||(7)|
where PCX and PCY represent the probabilities of agent X and agent Y continuing, respectively. Figures 9, 10, and 11 plot these utilities for all combinations of PCX and PCY, with combined utilities on the left, individual on the right. In each of the figures, the utility is plotted against the zero plane. In Figure 9, which plots utilities for the one round game, most combinations of probabilities yield positive combined utilities. Only combinations near (0, 0) and (1,1) produce negative outcomes. Likewise, a large portion of the probability combinations yield positive individual utilities.
|Figure 9. Utility Space for One-Round Games|
|Figure 10. Utility Space for Two-Round Games|
|Figure 11. Utility Space for Three-Round Games|
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