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  1. Jerome R. Busemeyer & Joseph G. Johnson (2008). Microprocess Models of Decision Making. In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press 302--321.
     
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  2. X. T. Wang & Joseph G. Johnson (2012). A Tri-Reference Point Theory of Decision Making Under Risk. Journal of Experimental Psychology: General 141 (4):743-756.
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    Joseph G. Johnson & Jerome R. Busemeyer (2001). Multiple-Stage Decision-Making: The Effect of Planning Horizon Length on Dynamic Consistency. Theory and Decision 51 (2/4):217-246.
    Many decisions involve multiple stages of choices and events, and these decisions can be represented graphically as decision trees. Optimal decision strategies for decision trees are commonly determined by a backward induction analysis that demands adherence to three fundamental consistency principles: dynamic, consequential, and strategic. Previous research found that decision-makers tend to exhibit violations of dynamic and strategic consistency at rates significantly higher than choice inconsistency across various levels of potential reward. The current research extends these findings under new conditions; (...)
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  4. Jerome R. Busemeyer, Joseph G. Johnson & Ryan K. Jessup (2006). Preferences Constructed From Dynamic Micro-Processing Mechanisms. In Sarah Lichtenstein & Paul Slovic (eds.), The Construction of Preference. Cambridge University Press 220--234.
     
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  5. Markus Raab & Joseph G. Johnson (2007). Expertise-Based Differences in Search and Option-Generation Strategies. Journal of Experimental Psychology: Applied 13 (3):158-170.
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  6. Joseph G. Johnson & Jerome R. Busemeyer (2005). A Dynamic, Stochastic, Computational Model of Preference Reversal Phenomena. Psychological Review 112 (4):841-861.
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