Computational models and organizational psychology: Opportunities abound

Organizational Psychology Review

Volumn 2, Issue 4, 2012, Pages 267-292

  Weinhardt, Justin M ^a   Vancouver, Jeffrey B ^a  

^a OHIO UNIVERSITY   (United States)

Author keywords

Cognition perception; Decision making; Statistics methods

Indexed keywords

EID: 84883331608     PISSN: 20413866     EISSN: 20413874     Source Type: Journal    
DOI: 10.1177/2041386612450455     Document Type: Article

References (152)

1. Abelson, R. P., Carroll, J. D. (1965). Computer simulation of individual belief systems. The American Behavioral Scientist, 8, 24–30.
2. Ackerman, P. L. (1988). Determinants of individual differences during skill acquisition: Cognitive abilities and information processing. Journal of Experimental Psychology: General, 117, 288–318.
3. Adner, R., Pólos, L., Ryall, M., & Sorenson, O. (2009). The case for formal theory. Academy of Management Review, 34, 201–208.
4. Anderson, J. R. (1983). The architecture of cognition. Hillsdale, NJ: Lawrence Erlbaum Associates.
5. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036–1060.
6. Atkinson, J. W., & Birch, D. (1970). The dynamics of action. New York, NY: Wiley.
7. Bagozzi, R. P., Bergami, M., Marzocchi, G. L., & Morandin, G. (2012). Customer–organization relationships: Development and test of a theory of extended identities. Journal of Applied Psychology, 97(1), 63–76.
8. Bandura, A., (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.
9. Barabási, A. L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435, 207–211.
10. Beal, D. J., & Weiss, H. M. (2003). Methods of ecological momentary assessment in organizational research. Organizational Research Methods, 6(4), 440–464.
11. Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models: Model building, testing, and illustration. Organizational Research Methods, 5(4), 362–387.
12. Boden, M. A. (2008). An evaluation of computational modeling in cognitive science. In R Sun (Ed.), The Cambridge handbook of computational psychology (pp. 667–683). New York, NY: Cambridge University Press.
13. Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the USA, 99, 7280–7287.
14. Borman, W. C., Klimoski, R. J., & Ilgen, D. R. (2003). Stability and change in industrial and organizational psychology. In W. C. Borman, R. J. Klimoski, & D. R. Ilgen (Eds.), Handbook of psychology: Industrial and organizational psychology (Vol. 12, pp. 1–17). Hoboken, NJ: John Wiley & Sons.
15. Bringsjord, S. (2008). Declarative/logic-based cognitive modeling. In R. Sun (Ed.), The Cambridge handbook of computational psychology (pp. 127–169). New York, NY: Cambridge University Press.
16. Bruderer, E., & Singh, J. V. (1996). Organizational evolution, learning, and selection: A genetic-algorithm-based model. Academy of Management Journal, 39(5), 1322–1349.
17. Busemeyer, J. R., & Diederich, A. (2010). Cognitive modeling. Los Angeles, CA: Sage.
18. Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara Gambling Task. Psychological Assessment, 14(3), 253–262.
19. Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432–459.
20. Camerer, C., Babcock, L., Loewenstein, G. & Thaler, R. (1997). Labor supply of New York City cab drivers: One day at a time. The Quarterly Journal of Economics, 112, 407–441.
21. Carley, K., & Hill, V. (2001). Structural change and learning within organizations. In A. Lomi & E. Larsen (Ed.), Dynamics of organizations: Computational modeling and organization theories (pp. 63–93). Menlo Park, CA: MIT Press.
22. Carley, K. M., Prietula, M. J., & Lin, Z. (1998). Design versus cognition: The interaction of agent cognition and organizational design on organizational performance. Journal of Artificial Societies and Social Simulation, 1(3), 1–19.
23. Cervone, D. (2004). The architecture of personality. Psychological Review, 111(1), 183–204.
24. Chan, D. (2002). Latent growth modeling. In F. Drawgow & N. Schmitt (Eds.), Measuring and analyzing behavior in organizations: Advances in measurement and data analysis (pp. 302–349). San Francisco, CA: Jossey-Bass.
25. Colquitt, J. A., LePine, J. A., Piccolo, R. F., Zapata, C. P., & Rich, B. L. (2012). Explaining the justice-performance relationship: Trust as exchange deepener or trust as uncertainty reducer? Journal of Applied Psychology, 97, 1–15.
26. Coovert, M. D., & Dorsey, D. W. (2000). Computational modeling with Petri nets: Solutions for individual and team systems. In D. R. Ilgen & C. L. Hulin (Eds.), Computational modeling of behavior in organizations: The third scientific discipline (pp. 163–188). Washington, DC: American Psychological Association.
27. Cronin, M. A., Gonzalez, C., & Sterman, J. D. (2009). Why don’t well-educated adults understand accumulation? A challenge to researchers, educators, and citizens. Organizational Behavior and Human Decision Processes, 108(1), 116–130.
28. Curran, P. J., & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583– 619.
29. Dalal, R. S., & Hulin, C. L. (2008). Motivation for what? A multivariate dynamic perspective of the criterion. In R. Kanfer, G. Chen, & R. D. Pritchard (Eds.), Work motivation: Past, present, and future (pp. 63–101). New York, NY: Routledge.
30. Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing theory through simulation methods. The Academy of Management Review, 32(2), 480–499.
31. Day, D.V., & Sin, H-P. (2011). Longitudinal tests of an integrative model of leader development: Charting and understanding developmental trajectories. Leadership Quarterly, 22, 545–560.
32. Desel, J., & Silva, J. (1998). Application and theory of Petri nets. New York, NY: Springer.
33. Diefendorff, J. M., & Lord, R. G. (2008). Self-regulation and goal striving processes. In R. Kanfer, G. Chen, & R. Pritchard (Eds.), Work motivation: Past, present, and future (pp. 151–196). Mahwah, NJ: Lawrence Erlbaum & Associates.
34. Drasgow, F. (2003). Intelligence and the workplace. In W. C. Borman, R. J. Klimoski, & D. R. Ilgen (Eds.), Handbook of psychology: Industrial and organizational psychology (Vol. 12, pp. 107–130). Hoboken, NJ: John Wiley & Sons.
35. Eberts, R. (1997). Cognitive modeling. In G. Sal-vendy (Ed.), Handbook of human factors and ergonomics (2nd ed., pp. 1328–1374). New York, NY: John Wiley & Sons.
36. Edwards, J. R. (1992). A cybernetic theory of stress, coping, and well-being in organizations. The Academy of Management Review, 17(2), 238–274.
37. Edwards, J. R., & Berry, J. W. (2010). The presence of something or the absence of nothing: Increasing theoretical precision in management research. Organizational Research Methods, 13(4), 668–689.
38. Forrester, J. (1968). The principles of systems. Cambridge, MA: Wright–Allen Press.
39. Gray, P. H., & Cooper, W. H. (2010). Pursuing failure. Organizational Research Methods, 13(4), 620–643.
40. Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition. In The Cambridge handbook of computational psychology (pp. 59–100). New York, NY: Cambridge University Press.
41. Hanisch, K. A. (2000). The impact of organizational interventions on behaviors: An examination of models of withdrawal. In D. R. Ilgen & C. L. Hulin (Eds.), Computational modeling of behavior in organizations: The third scientific discipline (pp. 33–68). Washington, DC: American Psychological Association.
42. Hanisch, K. A., Hulin, C. L., & Seitz, S. T. (1996). Mathematical/computational modeling of organizational withdrawal processes: Benefits, methods, and results. Research in Personnel and Human Resources Management, 14, 91–142.
43. Harrison, J. R., Lin, Z., Carroll, G. R., & Carley, K. M. (2007). Simulation modeling in organizational and management research. The Academy of Management Review, 32(4), 1229–1245.
44. Harrison, R., & Carroll, G. (2001). Modeling culture in organizations: Formulation and extension to ecological issues. In A. Lomi & E. Larsen (Ed.), Dynamics of organizations: Computational modeling and organization theories (pp. 37–63). Menlo Park, CA: MIT Press.
45. Hintzman, D. L. (1990). Human learning and memory: Connections and dissociations. Annual Review of Psychology, 41, 109–139.
46. Howell, W. C. (2003). Human factors and ergonomics. Handbook of psychology: Industrial and organizational psychology (Vol. 12, pp. 541–564). Hoboken, NJ: John Wiley & Sons.
47. Hulin, C. L., Henry, R. A., & Noon, S. L. (1990). Adding a dimension: Time as a factor in the generalizability of predictive relationships. Psychological Bulletin, 107(3), 328–340.
48. Hulin, C. L., & Ilgen, D. R. (2000). Introduction to computational modeling in organizations: The good that modeling does. In D. R. Ilgen & C. L. Hulin (Eds.), Computational modeling of behavior in organizations: The third scientific discipline (pp. 3–18). Washington, DC: American Psychological Association.
49. Hulin, C. L., & Judge, T. A. (2003). Job attitudes. In W. C. Borman, R. J. Klimoski, & D. R. Ilgen (Eds.), Handbook of psychology: Industrial and organizational psychology (Vol. 12, pp. 255–276). Hoboken, NJ: John Wiley & Sons.
50. Ilgen, D. R., & Hollenbeck, J. R. (1991). The structure of work: Job design and roles. Handbook of industrial and organizational psychology (2nd ed., Vol. 2, pp. 165–207). Palo Alto, CA: Consulting Psychologists Press.
51. Ilgen, D. R., & Hulin, C. L. (2000). Computational modeling of behavior in organizations: The third scientific discipline. Washington, DC: American Psychological Association.
52. Jagacinski, R. J., & Flach, J. M. (2003). Control theory for humans: Quantitative approaches to modeling performance. Mahwah, NJ: Lawrence Erlbaum Associates.
53. Johnson, M. D., Ilies, R., & Boles, T. L. (2012). Alternative reference points and outcome evaluation: The influence of affect. Journal of Applied Psychology, 97(1), 33–45.
54. Johnson-Laird, P., & Yang, Y. (2008). Mental logic, mental models, and simulations of human deductive reasoning. In R. Sun (Ed.), The Cambridge handbook of computational psychology (pp. 339–358) New York, NY: Cambridge University Press.
55. Kammeyer-Mueller, J., Wanberg, C. R., Glomb, T. M., & Ahlburg, D. (2005). The role of temporal shifts in turnover processes: It’s about time. Journal of Applied Psychology, 90(4), 644–658.
56. Kanfer, R. (2009). Work motivation: Advancing theory and impact. Industrial and Organizational Psychology: Perspectives on Science and Practice, 2(1), 118–127.
57. Katzell, R. A. (1994). Contemporary meta-trends in industrial and organizational psychology. In H. Triandis, M. D. Dunnette, & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (2nd ed., Vol. 4, pp. 1–89). Palo Alto, CA: Consulting Psychologists Press.
58. Keil, C. T., & Cortina, J. M. (2001). Degradation of validity over time: A test and extension of Ackerman’s model. Psychological Bulletin, 127(5), 673–697.
59. Klein, H. J., Austin, J. T., & Cooper, J. T. (2008). Goal choice and decision processes. In R. Kanfer, G. Chen, & R. Pritchard (Eds.), Work motivation: Past, present, and future (SIOP Frontiers series, pp. 101–150). New York, NY: Routledge Academic.
60. Klein, K. J., & Kozlowski, S. W. J. (2000). Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions. San Francisco, CA: Jossey-Bass.
61. Kleinmuntz, B. (1990). Why we still use our heads instead of formulas: Toward an integrative approach. Psychological Bulletin, 107, 296–310.
62. Kluger, A. N., & DeNisi, A. (1996). Effects of feedback intervention on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284.
63. Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30–43.
64. Kristof-Brown, A., & Guay, R. P. (2011). Person– environment fit. In S. Zedeck (Ed.), APA handbook of industrial and organizational psychology, Vol 3: Maintaining, expanding, and contracting the organization (pp. 3–50). Washington, DC: American Psychological Association.
65. Kuljanin, G., & DeShon, R. P. (2011). SynthTeam: A computational model of intrateam dynamics and performance. Working paper. Michigan State University.
66. Latané, B. (2000). Pressures to uniformity and the evolution of cultural norms: Modeling dynamic social impact. In D. R. Ilgen & C. L. Hulin (Eds.), Computational modeling of behavior in organizations: The third scientific discipline. (pp. 189–220). Washington, DC: American Psychological Association.
67. Latham, G. P., & Pinder, C. C. (2005). Work motivation theory and research at the dawn of the twenty-first century. Annual Review of Psychology, 56, 485–516.
68. Lewandowsky, S., & Farrell, S. (2010). Computational modeling in cognition: Principles and practice. Thousand Oaks, CA: Sage.
69. Loch, C. H, Huberman, B. A., & Ülkü, S. (2001). Multi-dimensional status competition and group performance. In A. Lomi & E. Larsen (Ed.), Dynamics of organizations: Computational modeling and organization theories (pp. 119–141). Menlo Park, CA: MIT Press.
70. Locke, E. A. (1976). The nature and causes of job satisfaction. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1297–1349). Chicago, IL: Rand McNally.
71. Locke, E. A., & Latham, G. P. (1990). A theory of goal setting & task performance. Englewood Cliffs, NJ: Prentice-Hall.
72. Locke, E. A., & Latham, G. P. (2004). What should we do about motivation theory? Six recommendations for the twenty-first century. The Academy of Management Review, 29(3), 388–403.
73. Loewenstein, G. F., Rick, S., & Cohen, J. (2008). Neuroeconomics. Annual Review of Psychology, 59, 1–26.
74. Lomi, A., & Larsen, E. (1996). Interacting locally and evolving globally: A computational approach to the dynamics of organizational populations. Academy of Management Journal, 39, 1287–1321.
75. Lomi, A., & Larsen, E. (2001). Dynamics of organizations: Computational modeling and organization theories. Menlo Park, CA: AAAI Press/The MIT Press.
76. Lord, R. G., Hanges, P. J., & Godfrey, E. G. (2003). Integrating neural networks into decision-making and motivational theory: Rethinking VIE theory. Canadian Psychology/Psychologie Canadienne, 44(1), 21–38.
77. Lucas, R. E., Jr., & Rapping, L. A. (1969). Real wages, employment, and inflation. Journal of Political Economy, 77(5), 721–754.
78. Luce, R. D. (1995). Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46, 1–26.
79. March, J. G. (1991). How decisions happen in organizations. Human–Computer Interaction, 6(2), 95–117.
80. Martell, R. F., Lane, D. M., & Emrich, C. (1996). Male–female differences: A computer simulation. American Psychologist, 51(2), 157–158.
81. McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review, 88(5), 375–407.
82. McClelland, J. L., & Rumelhart, D. E., & The PDP Research Group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Volume II. Cambridge: MIT Press.
83. Meehl, P. E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46(4), 806–834.
84. Mischel, W., & Shoda, Y. (1995). A cognitive-affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102(2), 246–268.
85. Mischel, W., & Shoda, Y. (1998). Reconciling processing dynamics and personality dispositions. Annual Review of Psychology, 49, 229–258.
86. Mitchell, T. R., & James, L. R. (2001). Building better theory: Time and the specification of when things happen. Academy of Management Review, 26, 530–547.
87. Motowidlo, S. J. (2003). Job performance. In W. C. Borman, R. J. Klimoski, & D. R. Ilgen (Eds.), Handbook of psychology: Industrial and organizational psychology, Vol. 12. (pp. 39–53). Hoboken, NJ: John Wiley & Sons.
88. Myung, I. J. (2003). Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology, 47(1), 90–100.
89. Navarro, J., & Arrieta, C. (2010). Chaos in human behavior: The case of work motivation. The Spanish Journal of Psychology, 13(1), 244–256.
90. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.
91. Newell, A., & Simon, H. (1963). Computers in psychology. In R. Luce, R. Bush, & E. Galanter (Eds.), Handbook of mathematical psychology, Volume I (pp. 361–428). New York, NY: Wiley.
92. Nisbett, R. E. (2009). Intelligence and how to get it: Why schools and cultures count. New York, NY: W. W. Norton.
93. Pew, R. W., & Mavor, A. S. (1998). Modeling human and organizational behavior. Washington, DC: National City Press.
94. Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The theory, design, and analysis of change. Journal of Management, 36(1), 94–120.
95. Popper, K. (1969). Conjectures and refutations: The growth of scientific knowledge. London, UK: Routledge.
96. Powers, W. T. (1973). Behavior. The control of perception. Chicago, IL: Aldine.
97. Powers, W. T. (1991). Commentary on Bandura’s ‘‘human agency.’’ American Psychologist, 46(2), 151–153.
98. Railsback, S. F., & Grimm, V. (2011). Agent-based and individual-based modeling: A practical introduction. Princeton, NJ: Princeton University Press.
99. Read, S. J., & Miller, L. C. (2002). Virtual personalities: A neural network model of personality. Personality and Social Psychology Review, 6(4), 357–369.
100. Read, S. J., & Monroe, B. M. (2008). Computational models in personality and social psychology. In R. Sun (Ed.), The Cambridge handbook of computational psychology (pp. 505–529). New York, NY: Cambridge University Press.
101. Repenning, N. P. (2003). Selling system dynamics to (other) social scientists. System Dynamics Review, 19(4), 300–327.
102. Riley, W., Rivera, D., Atienza, A., Nilsen, W., Allison, S., & Mermelstein, R. (2011). Health behavior models in the age of mobile interventions: Are our theories up to the task? Translational Behavioral Medicine, 1(1), 53–71.
103. Rivkin, J. W. (2001). Reproducing knowledge: Replication without imitation at moderate complexity. Organization Science, 12(3), 274–293.
104. Rogers, T. T., & McClelland, J. L. (2004). Semantic cognition: A parallel distributed processing approach. Cambridge: MIT Press.
105. Rosenbaum, D. A. (2007). MATLAB for behavioral scientists. Mahwah, NJ: Lawrence Erlbaum Associates.
106. Rosenblueth, A., Wiener, N., & Bigelow, J. (1943). Behavior, purpose, and teleology. Philosophy of Science, 10, 18–24.
107. Rumelhart, D. E., & McClelland, J. L., & The PDP Research Group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Volume I. Cambridge: MIT Press.
108. Sackett, P. R., & Roth, L. (1991). A Monte Carlo examination of banding and rank order methods of test score use in personnel selection. Human Performance, 4(4), 279–295.
109. Sackett, P. R., & Roth, L. (1996). Multi-stage selection strategies: A Monte Carlo investigation of effects on performance and minority hiring. Personnel Psychology, 49(3), 549–572.
110. Sackett, P. R., & Wilk, S. L. (1994). Within-group norming and other forms of score adjustment in preemployment testing. American Psychologist, 49(11), 929–954.
111. Sastry, M. A. (1997). Problems and paradoxes in a model of punctuated organizational change. Administrative Science Quarterly, 42(2), 237–275.
112. Scherbaum, C. A., & Vancouver, J. B. (2010). If we produce discrepancies, then how? Testing a computational process model of positive goal revision. Journal of Applied Social Psychology, 40(9), 2201–2231.
113. Scherbaum, C. A., Vancouver, J. B., & Finlinson, S. (2003, April). Applying computational and multilevel modeling to Ackerman’s skill acquisition model. Poster presented at the annual meeting of the Society for Industrial and Organizational Psychology, Orlando, FL.
114. Schmidt, A. M., & DeShon, R. P. (2007). What to do? The effects of discrepancies, incentives, and time on dynamic goal prioritization. Journal of Applied Psychology, 92, 928–941.
115. Schneider, B. (1987). The people make the place. Personnel Psychology, 40(3), 437–453.
116. Schöner, G. (2008). Dynamical systems approaches to cognition. In R. Sun (Ed.), The Cambridge handbook of computational psychology (pp. 101–126). New York, NY: Cambridge University Press.
117. Schonfeld, I. S., & Rindskopf, D. (2007). Hierarchical linear modeling in organizational research: Longitudinal data outside the context of growth modeling. Organizational Research Methods, 10(3), 417–429.
118. Schultze, T., Pfeiffer, F., & Schulz-Hardt, S. (2012). Biased information processing in the escalation paradigm: Information search and information evaluation as potential mediators of escalating commitment. Journal of Applied Psychology, 97(1), 16–32.
119. Schwab, D. P., & Olson, C. A. (2000). Simulating effects of pay-for-performance systems on pay–performance relationships. In D. R. Ilgen & C. L. Hulin (Eds.), Computational modeling of behavior in organizations: The third scientific discipline (pp. 115–134). Washington, DC: American Psychological Association.
120. Senge, P. M. (1990). Catalyzing systems thinking within organizations. In F. Masaryk (Ed.), Advances in organization development (Vol. 1, pp. 197–246). Westport, CT: Ablex Publishing.
121. Simon, H. A. (1992). Decision making and problem solving. In M. Zey (Ed.), Decision making: Alternatives to rational choice models (pp. 32–53). Thousand Oaks, CA: Sage Publications.
122. Stasser, G. (2000). Information distribution, participation, and group decision: Explorations with the DISCUSS and SPEAK models. In D. R. Ilgen & C. L. Hulin (Eds.), Computational modeling of behavior in organizations: The third scientific discipline (pp. 135–161). Washington, DC: American Psychological Association.
123. Steel, P., & König, C. J. (2006). Integrating theories of motivation. Academy of Management Review, 31, 889–913.
124. Steel, R. P. (2002). Turnover theory at the empirical interface: Problems of fit and function. Academy of Management Review, 27, 346–360.
125. Steele-Johnson, D., Osburn, H. G., & Pieper, K. F. (2000). A review and extension of current models of dynamic criteria. International Journal of Selection and Assessment, 8(3), 110–136.
126. Sterman, J. D. (1989). Misperceptions of feedback in dynamic decision making. Organizational Behavior and Human Decision Processes, 43(3), 301–335.
127. Sterman, J. D. (2000). Business dynamics. Systems thinking and modeling for a complex world. New York, NY: McGraw-Hill.
128. Sturman, M. C., Shao, L., & Katz, J. H. (2012). The effect of culture on the curvilinear relationship between performance and turnover. Journal of Applied Psychology, 97(1), 46–62.
129. Sun, R. (2006). The CLARION cognitive architecture: Extending cognitive modeling to social simulation. In R. Sun (Ed.), Cognition and multi-agent interaction: From cognitive modeling to social simulation. (pp. 79–99). New York, NY: Cambridge University Press.
130. Sun, R. (2008). The Cambridge handbook of computational psychology. New York, NY: Cambridge University Press.
131. Sun, R., Coward, L. A., & Zenzen, M. J. (2005). On levels of cognitive modeling. Philosophical Psychology, 18(5), 613–637.
132. Sun, R., & Naveh, I. (2004). Simulating organizational decision-making using a cognitively realistic agent model. Journal of Artificial Societies and Social Simulation, 7(3). Retrieved from http://jasss.soc.surrey.ac.uk/7/3/5.html
133. Sweeney, L. B., & Sterman, J. D. (2007). Thinking about systems: Student and teacher conceptions of natural and social systems. System Dynamics Review, 23(2–3), 285–312.
134. Taber, C. S., & Timpone, R. J. (1996). Computational modeling. Thousand Oaks, CA: Sage Publications.
135. Thomas, M. S. C., & McClelland, J. L. (2008). Connectionist models of cognition. In R. Sun (Ed.), The Cambridge handbook of computational psychology (pp. 23–58). New York, NY: Cambridge University Press.
136. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.
137. Vancouver, J. B. (2005). The depth of history and explanation as benefit and bane for psychological control theories. Journal of Applied Psychology, 90(1), 38–52.
138. Vancouver, J. B. (2008). Integrating self-regulation theories of work motivation into a dynamic process theory. Human Resource Management Review, 18(1), 1–18.
139. Vancouver, J. B. (2012). Applying control theory to sequential foreign policy decision-making. In C. F. Hermann (Ed.), When things go wrong: Foreign policy decision-making under adverse feedback (pp. 142–173). New York, NY: Routledge Press.
140. Vancouver, J. B., Putka, D. J., & Scherbaum, C. A. (2005). Testing a computational model of the goal-level effect: An example of a neglected methodology. Organizational Research Methods, 8(1), 100–127.
141. Vancouver, J. B., & Scherbaum, C. A. (2008). Do we self-regulate actions or perceptions? A test of two computational models. Computational and Mathematical Organization Theory, 14, 1–22.
142. Vancouver, J. B., Tamanini, K. B., & Yoder, R. J. (2010). Using dynamic computational models to reconnect theory and research: Socialization by the proactive newcomer as example. Journal of Management, 36(3), 764–793.
143. Vancouver, J. B., & Weinhardt, J. M. (in press). Modeling the mind and the milieu: Computational modeling for micro-level organizational scholars. Organizational Research Methods.
144. Vancouver, J. B., Weinhardt, J. M., & Schmidt, A. M. (2010). A formal, computational theory of multiple-goal pursuit: Integrating goal-choice and goal-striving processes. Journal of Applied Psychology, 95(6), 985–1008.
145. Vroom, V. H. (1964). Work and motivation. Oxford, UK: Wiley.
146. Wallsten, T. S., Pleskac, T. J., Lejuez, C. W., (2005). Modeling a sequential risk-taking task. Psychological Review, 112, 862–880.
147. Weinhardt, J. M., & Vancouver, J. B. (2012). Intelligent interventions. Industrial and Organizational Psychology: Perspectives on Science and Practice, 5, 181–184.
148. Weiss, H. M., & Cropanzano, R. (1996). Affective events theory: A theoretical discussion of the structure, causes and consequences of affective experiences at work. In B. M. Staw & L. L. Cummings (Eds.), Research in organizational behavior: An annual series of analytical essays and critical reviews (Vol. 18, pp. 1–74). Greenwich, CT: JAI Press.
149. Yechiam, E., & Busemeyer, J. R. (2008). Evaluating generalizability and parameter consistency in learning models. Games and Economic Behavior, 63(1), 370–394.
150. Yechiam, E., Busemeyer, J. R., Stout, J. C., & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits. Psychological Science, 16(12), 973–978.
151. Zickar, M. J. (2000). Modeling faking on personality tests. In D. R. Ilgen & C. L. Hulin (Eds.), Computational modeling of behavior in organizations: The third scientific discipline (pp. 95–113). Washington, DC: American Psychological Association.
152. Zickar, M. J., & Slaughter, J. E. (2002). Computational modeling. In S. G. Rogelberg (Ed.), Handbook of research methods in industrial and organizational psychology (pp. 184–197). Malden, MA: Blackwell Publishing.