Individual Bounded Rationality and Crowdsourcing Performance: An Agent-based Simulation

doi:10.12005/orms.2018.0286

Abstract

Abstract: The advancement of internet makes firms able to use collective intelligence to solve complex problems with the help of crowdsourcing by combining knowledge of outside participants. However, compared to inside employees, the bounded rationality level of outside participants is relatively low, which becomes a major concern of the firms. Therefore, how to recruit participants is a key issue for firms to implement crowdsourcing. To solve the problem, by introducing individual bounded rationality, an agent-based model that simulates problem solving process of tournament-based crowdsourcing is constructed by extending the NK model to explore the effects of the level of bounded rationality, the systematization of bounded rationality and the standard deviation of bounded rationality level on crowdsourcing performance. The results of simulation experiments suggest that the level of bounded rationality has a significant positive effect on crowdsourcing performance, especially in cases where the task complexity is high. The systematization of bounded rationality and the standard deviation of bounded rationality level also have a positive effect on the crowdsourcing performance, but they depend on the level of bounded rationality. Therefore, when there is a higher complexity task, and if the firm pursues the overall improvement of all solutions, the best strategy is to recruit individuals with higher bounded rationality level and form a group with higher systematization of bounded rationality and higher standard deviation of bounded rationality level; but if the firm pursues a small number of high-quality solutions, the best strategy is to recruit individuals with higher bounded rationality level and form a group with lower systematization of bounded rationality and higher standard deviation of bounded rationality level.

Key words: tournament-based crowdsourcing, the level of bounded rationality, the systematization of bounded rationality, the standard deviation of bounded rationality level, agent-based simulation

摘要： 如何筛选有限理性参与者是企业实施众包的关键问题。通过引入个体有限理性,扩展了经典的NK模型,构建了模拟竞赛式众包问题解决过程的多主体仿真模型,研究了个体有限理性水平、个体有限理性系统化程度以及个体有限理性水平标准差对众包绩效的影响。仿真结果显示,个体有限理性水平对提高众包绩效有显著的正向影响,尤其是在任务复杂性较高的情况下;个体有限理性系统化程度和个体有限理性水平标准差对众包绩效也有正向影响,但依赖于个体有限理性水平。当企业有一个复杂性较高的任务时,如果追求所有方案的整体改善,企业需要招募有限理性水平较高的个体,并组成有限理性系统化程度较高且有限理性水平标准差较大的群体;如果追求少数优质方案,企业需要招募有限理性水平较高的个体,并组成有限理性系统化程度较低且有限理性水平标准差较大的群体。

关键词: 竞赛式众包, 有限理性水平, 有限理性系统化程度, 有限理性水平标准差, agent-based仿真

CLC Number:

F270.7

YAN Jie, LIU Ren-jing. Individual Bounded Rationality and Crowdsourcing Performance: An Agent-based Simulation[J]. Operations Research and Management Science, 2018, 27(12): 115-124.

严杰,刘人境. 基于Agent仿真的个体有限理性与众包绩效的关系研究[J]. 运筹与管理, 2018, 27(12): 115-124.

References

[1] Howe J. The rise of crowdsourcing[J]. Wired magazine, 2006, 14(6): 1-4.
[2] Brabham D C. Crowdsourcing as a model for problem solving: an introduction and cases[J]. Convergence: The International Journal of Research into New Media Technologies, 2008, 14(1): 75-90.
[3] Palacios M, Martinez-Corral A, Nisar A, et al. Crowdsourcing and organizational forms: emerging trends and research implications[J]. Journal of Business Research, 2016, 69(5): 1834-1839.
[4] Guazzini A, Vilone D, Donati C, et al. Modeling crowdsourcing as collective problem solving[J]. Scientific reports, 2014, 5(2): 16557-16557.
[5] Simon H A. Administrative behavior[M]. New York: Free Press, 1965.
[6] Nelson R R, Winter S G. Evolutionary theorizing in economics[J]. The journal of economic perspectives, 2002, 16(2): 23-46.
[7] March J G, Simon H A. Organizations[J]. American Journal of Sociology, 1959, 65(2): 105-132.
[8] Cyert R M, March J G. A behavioral theory of the firm[J]. American Journal of Sociology, 1965, 71(2): 81-95.
[9] Simon H A. A behavioral model of rational choice[J]. Quarterly Journal of Economics, 1955, 69(1): 99-118.
[10] Bitektine A. Toward a theory of social judgments of organizations: the case of legitimacy, reputation, and status[J]. Academy of Management Review, 2011, 36(1): 151-179.
[11] Huang TL, Allon G, Bassamboo A. Bounded rationality in service systems[J]. M&Som-Manufacturing & Service Operations Management, 2013, 15(2): 263-279.
[12] Mallard G. Modelling cognitively bounded rationality: an evaluative taxonomy[J]. Journal of Economic Surveys, 2012, 26(4): 674-704.
[13] 孙茜,刘海波,杨绪勇,等.创新众包平台对接包方中标率的影响机制研究[J].科学学研究,2016,34(2):279-287.
[14] Stiles E, Cui X H. Workings of collective intelligence within open source communities[C]. Advances in Social Computing, Proceedings, 2010, 6007: 282-289.
[15] Gavetti G, Levinthal D. Looking forward and looking backward: cognitive and experiential search[J]. Administrative Science Quarterly, 2000, 45(1): 113-137.
[16] Afuah A, Tucci C L. Crowdsourcing as a solution to distant search[J]. Academy of Management Review, 2012, 37(3): 355-375.
[17] Chiu C M, Liang T P, Turban E. What can crowdsourcing do for decision support?[J]. Decision Support Systems, 2014, 65(3): 40-49.
[18] Estellés-Arolas E, González-Ladrón-De-Guevara F. Towards an integrated crowdsourcing definition[J]. Journal of Information science, 2012, 38(2): 189-200.
[19] Zhao Y, Zhu Q. Evaluation on crowdsourcing research: current status and future direction[J]. Information Systems Frontiers, 2014, 16(3): 417-434.
[20] Claussen J, Kretschmer T, Stieglitz N. Vertical scope, turbulence, and the benefits of commitment and flexibility[J]. Management Science, 2014, 61(4): 915-929.
[21] Bayus B L. Crowdsourcing new product ideas over time: an analysis of the dell ideastorm community[J]. Management science, 2013, 59(1): 226-244.
[22] Gavetti G, Levinthal D A, Rivkin J W. Strategy making in novel and complex worlds: the power of analogy[J]. Strategic Management Journal, 2005, 26(8): 691-712.
[23] Simon H A. The architecture of complexity[J]. Proceedings of the American philosophical society, 1962, 106(6): 467-482.
[24] Levinthal D, March J G. A model of adaptive organizational search[J]. Journal of Economic Behavior & Organization, 1981, 2(4): 307-333.
[25] Levinthal D A. Adaptation on rugged landscapes[J]. Management science, 1997, 43(7): 934-950.
[26] Kauffman S A. The origins of order: self-organization and selection in evolution[M]. Oxford University Press, USA, 1993.
[27] Siggelkow N, Rivkin J W. Speed and search: designing organizations for turbulence and complexity[J]. Organization Science, 2005, 16(2): 101-122.
[28] Ethiraj S K, Levinthal D. Modularity and innovation in complex systems[J]. Management science, 2004, 50(2): 159-173.
[29] Fleming L, Sorenson O. Science as a map in technological search[J]. Strategic Management Journal, 2004, 25(8-9): 909-928.
[30] Almirall E, Casadesus-Masanell R. Open versus closed innovation: a model of discovery and divergence[J]. Academy of management review, 2010, 35(1): 27-47.
[31] Lazer D, Friedman A. The network structure of exploration and exploitation[J]. Administrative Science Quarterly, 2007, 52(4): 667-694.
[32] Simon H A. The sciences of the artificial[M]. MIT press, 1996.
[33] Vuculescu O, Bergenholtz C. How to solve problems with crowds: a computer-based simulation model[J]. Creativity and Innovation Management, 2014, 23(2): 121-136.
[34] Shafir E, LeBoeuf R A. Rationality[J]. Annual review of psychology, 2002, 53(1): 491-517.
[35] Simon H A, Newell A. Human problem solving: the state of the theory in 1970[J]. American Psychologist, 1971, 26(2): 145-159.
[36] Milgrom P, Roberts J. The economics of modern manufacturing: technology, strategy, and organization[J]. American Economic Review, 1990, 80(3): 511-528.
[37] Holland P W. Statistics and causal inference[J]. Journal of the American statistical Association, 1986, 81(396): 945-960.
[38] 马骏,席酉民,曾宪聚.战略的选择:管理认知与经验搜索[J].科学学与科学技术管理,2007,28(11):114-119.
[39] Thagard P. Mind: Introduction to cognitive science[M]. Cambridge, MA: MIT press, 1996.
[40] Louis M R, Sutton R I. Switching cognitive gears: from habits of mind to active thinking[J]. Human relations, 1991, 44(1): 55-76.
[41] 严杰,刘人境,徐搏. 集中,分组,分组后集中:探究三种群体结构对群体决策能力的影响[J].运筹与管理,2016,25(05):6-14
[42] Walter T, Back A. Towards measuring crowdsourcing success: an empirical study on effects of external factors in online idea contest[J]. The 6th Mediterranean conference on information systems, 2011. 1-12.
[43] Javadi Khasraghi H, Aghaie A. Crowdsourcing contests: understanding the effect of competitors’ participation history on their performance[J]. Behaviour & Information Technology, 2014, 33(12): 1383-1395.
[44] Kerr NL, MacCoun RJ, Kramer GP. Bias in judgment: comparing individuals and groups[J]. Psychological Review, 1996, 103(4): 687-719.
[45] Rivkin J W, Siggelkow N. Patterned interactions in complex systems: implications for exploration[J]. Management Science, 2007, 53(7): 1068-1085.
[46] Oyama K, Learmonth G, Chao R. Applying complexity science to new product development: modeling considerations, extensions, and implications[J]. Journal of Engineering and Technology Management, 2015, 35(5): 1-24.
[47] Miller K D, Zhao M, Calantone R J. Adding interpersonal learning and tacit knowledge to March’s exploration-exploitation model[J]. Academy of Management Journal, 2006, 49(4): 709-722.
[48] Kim T, Rhee M. Exploration and exploitation: internal variety and environmental dynamism[J]. Strategic Organization, 2009, 7(1): 11-41.
[49] Kane GC, Alavi M. Information technology and organizational learning: an investigation of exploration and exploitation processes[J]. Organization Science, 2007, 18(5): 796-812.
[50] Zollman K J S. The epistemic benefit of transient diversity[J]. Erkenntnis, 2010, 72(1):17-35.
[51] LiCalzi M, Surucu O. The power of diversity over large solution spaces[J]. Management Science, 2012, 58(7): 1408-1421.