The “Resident’s Dilemma”?

Summary

Objectives: Medical residents engage in formal and informal education interactions with fellow residents during the working day, and can choose whether to spend time and effort on such interactions. Time and effort spent on such interactions can bring learning and personal satisfaction to residents, but may also delay completion of clinical work.

Methods: Using hypothetical cases, we assessed the values and strategies of internal medicine residents at one hospital for both cooperative and non-cooperative education interactions with fellow residents. We then used these data and cellular automata models of two-person games to simulate repeated interactions between residents, and to determine which strategies resulted in greatest accrued value. We conducted sensitivity analyses on several model parameters, to test the robustness of dominant strategies to model assumptions.

Results: Twenty-nine of the 57 residents (50.9%) valued cooperation more than non-cooperation no matter what the other resident did during the current interaction. Similarly, thirty-six residents (63.2%) endorsed an unconditional always-cooperate strategy no matter what the other resident had done during their previous interaction. In simulations, an always-cooperate strategy accrued more value (776.42 value units) than an aggregate of strategies containing non-cooperation components (675.0 value units, p = 0.052). Only when the probability of strategy errors reached 50%, or when values were re-ordered to match those of a Prisoner's Dilemma, did non-cooperation-based strategies accrue the most value.

Conclusions: Cooperation-based values and strategies were most frequent among our residents, and dominated in simulations of repeated education interactions between them.

Keywords

Medical education - simulation - cellular automata - game theory - Prisoner‘s Dilemma

^* S. J. Weiner was supported by the Robert Wood Johnson Generalist Physician Faculity Scholars Program, and the Veterans Health Administration.

• References

• 1 Tonesk X. The house officer as a teacher: what schools expect and measure. J Med Educ 1979; 54: 613-6.
• 2 Boex J, Leahy P. Understanding residents’ work: moving beyond counting hours to assessing educational value. Acad Med 2003; 78: 939-44.
• 3 Rapoport A.. Two-Person Game Theory: The Essential Ideas. Ann Arbor: University of Michigan Press 1966
• 4 Axelrod R.. The Evolution of Cooperation. Basic Books 1984
• 5 Cooper R. et al Cooperation without reputation: experimental evidence from Prisoner’s Dilemma games. Games Econ Behav 1996; 12: 187-218.
• 6 Johnson DD. et al Individual variation evades the Prisoner’s Dilemma. Evol Biol 2002; 2: 15
• 7 Nowak MA, Sigmund K. Tit-for-Tat in heterogeneous populations. Nature 1992; 355: 250-3.
• 8 Nowak MA, Sigmund K. A strategy of win-stay, lose-shift that outperforms Tit-for-Tat in the Prisoner’s Dilemma. Nature 1993; 364: 56-8.
• 9 Wedekind C, Manfred M. Human cooperation in the simultaneous and the alternating Prisoner's Dilemma: Pavlov versus generous Tit-for-Tat. Proc Natl Acad Sci 1996; 93: 2686-9.
• 10 Kreps D, Milgrom P, Roberts J, Wilson R. Rational cooperation in the finitely repeated Prisoner’s Dilemma. J Econ Theory 1982; 17: 245-52.
• 11 Farmer D, Toffoli T, Wolfram S. (eds.) Cellular Automata. Physica D 1984 10. Nos. 1 and 2
• 12 Gaylord RJ, Nishidate K.. Modeling Nature: Cellular Automata Simulations with Mathematica. New York: Springer-Verlag 1996
• 13 Gaylor RJ, D’Andria LJ. Simulating Society: A Mathematica Toolkit for Modeling Socioeconomic Behavior. New York: Springer-Verlag 1998
• 14 Oono Y, Puri S. Computationally efficient modeling of ordering of quenched phases. Phys Rev Let 1987; 58: 836-9.
• 15 Wu D, Ono K, Hosaka H, He B. Simulation of body surface Laplacian maps during ventricular pacing in a 3D inhomogeneous heart-torso model. Methods Inf Med 2000; 39: 196-9.
• 16 Schonfisch B. Propagation of fronts in cellular automata. Physica D 1995; 80: 433-50.
• 17 Epstein JM, Axtell R.. Growing Artificial Societies. Cambridge, Massachusetts: Brookings Institute Press/MIT Press 1996
• 18 Zerbe WJ, Paulhus DL. Socially desirable responding in organizational behavior: a reconception. Acad Management Rev 1987; 12: 250-64.
• 19 Mathematica 3.0.1. Wolfram Research, Inc. Champaign, Illinois. 1997
• 20 Forrest S. Genetic algorithms: principles of natural selection applied to computation. Science 1993; 261: 872-8.
• 21 Jacob C. Illustrating Evolutionary Computation with Mathematica. San Diego, California: Academic Press 2001
• 22 Heckerling PS, Gerber BS, Tape TG, Wigton RS. Selection of predictor variables for pneumonia using neural networks and genetic algorithms. Meth Inform Med 2005; 44: 89-97.
• 23 Hofstadter DR. The Prisoner’s Dilemma and the Evolution of Cooperation. From: Metamagical Themas: Questing for the Essence of Mind and Pattern. Toronto: Bantam Books 1985; 715-34.
• 24 Green MJ. et al Lying to each other: when internal medicine residents use deception with their colleagues. Arch Intern Med 2000; 160: 2317-23.
• 25 Boone C. et al The impact of personality on behavior in five Prisoner’s Dilemma games. J Econ Psychol 1999; 20: 343-77.
• 26 Peabody JW. et al Measuring the quality of physician practice by using clinical vignettes: a prospective validation study. Ann Intern Med 2004; 141: 771-80.
• 27 Milinski M, Wedekind C. Working memory constrains human cooperation in the Prisoner’s Dilemma. Proc Natl Acad Sci 1998; 95: 13755-8.