A Preliminary Study of Human Decision-Making, Risk Attitude, and Social Preference on Knowledge Management

被引:0
|
作者
Gu, Jessica [1 ,2 ]
Huang, Ji-Ping [3 ,4 ]
Chen, Yu [1 ]
机构
[1] Univ Tokyo, Grad Sch Frontier Sci, Tokyo, Japan
[2] Japan Soc Promot Sci, Tokyo, Japan
[3] Fudan Univ, Dept Phys, Shanghai, Peoples R China
[4] Fudan Univ, State Key Lab Surface Phys, Shanghai, Peoples R China
基金
日本学术振兴会;
关键词
Organizational performance; Incentive system; Risk and uncertainty; Competition and cooperation; Agent-based modeling;
D O I
10.1007/978-3-319-60591-3_27
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study aims to introduce a knowledge management (KM) incentive system that induces both monetary and social incentives; probe into the human decision-making nature and attitude towards risks and uncertainty; elucidate the causality between agent's KM motives at microscopic level and organizational outcomes at macroscopic level through strategic individual efforts and social interactions; and unfold the dynamic interplays between intangibles and tangibles. We firstly designed an organizational KM conceptual model with induced monetary incentives that feature: high risk high return for independent effort on innovation (creating new knowledge); low risk low return for dependent effort on imitation (acquiring shared knowledge); and a knowledge bonus which is contributed by collective cooperation and divided based on individual knowledge uniqueness level. Since risks and uncertainty are incorporated, agents have to utilize bounded rationality, psychologically reason upon equal expected utilities, and form strategies when facing two dilemmas: risk seeking vs. loss aversion and competition vs. cooperation. Secondly, we developed a gaming software and implemented the KM model in behavioral experiments to trace the endogenously evolving choices of agents under exogenous policies, capture the dynamic interactions, and observe the emergent properties at macroscopic level through iterations. On top a baseline control setting, three treatments with different interventions were carried out and compared against the baseline. With the empirical evidence obtained, the proposed KM incentive system demonstrated fairness, practicability, and effectiveness. Thirdly, we implemented the KM model into agent-based simulation for systemic prediction and optimization. Through combining the behavioral experiments and agent-based simulation. The microscopic human factors in organizational knowledge management are explored in-depth and the impact on the macroscopic organizational outcomes is revealed and elucidated.
引用
收藏
页码:297 / 311
页数:15
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