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  1.  9
    Interacting with others while reacting to the environment.Ilan Fischer, Simon A. Levin, Daniel I. Rubenstein, Shacked Avrashi, Lior Givon & Tomer Oz - 2022 - Behavioral and Brain Sciences 45.
    Here, we revise Pietraszewski's model of groups by assigning participant pairs with two triplets, denoting: the type of game that models the interaction, its critical switching point between alternatives, and the perception of strategic similarity with the opponent. These triplets provide a set of primitives that accounts for individuals' strategic motivations and observed behaviors.
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  2.  6
    The evolution of peace (and war) is driven by an elementary social interaction mechanism.Ilan Fischer, Shacked Avrashi & Lior Savranevski - 2024 - Behavioral and Brain Sciences 47:e7.
    Here we revise Glowacki's model by proposing a simple and empirically tested mechanism that is applicable to a comprehensive set of social interactions. This parsimonious mechanism accounts for the choice of both cooperative and peaceful alternatives and explains when each choice benefits the interacting parties. It is proposed that this mechanism is key to the evolution of both peace and conflict.
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  3.  60
    The Emergence of Reactive Strategies in Simulated Heterogeneous Populations.Ilan Fischer - 2003 - Theory and Decision 55 (4):289-314.
    The computer simulation study explores the impact of the duration of social impact on the generation and stabilization of cooperative strategies. Rather than seeding the simulations with a finite set of strategies, a continuous distribution of strategies is being defined. Members of heterogeneous populations were characterized by a pair of probabilistic reactive strategies: the probability to respond to cooperation by cooperation and the probability to respond to defection by cooperation. This generalized reactive strategy yields the standard TFT mechanism, the All-Cooperate, (...)
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  4.  44
    The Group Calibration Index: a group-based approach for assessing forecasters' expertise when external outcome data are missing. [REVIEW]Ilan Fischer & Ravid Bogaire - 2012 - Theory and Decision 73 (4):671-685.
    The Group Calibration Index (GCI) provides a means of assessing the quality of forecasters’ predictions in situations that lack external feedback or outcome data. The GCI replaces the missing outcome data with aggregated ratings of a well-defined reference group. A simulation study and two experiments show how the GCI classifies forecaster performance and distinguishes between forecasters with restricted information and those with complete information. The results also show that under certain circumstances, where members of the reference group have high-quality information, (...)
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