智能组织中的人机协同决策：基于人机内部兼容性的研究探索

doi:10.3724/SP.J.1042.2022.02619

摘要/Abstract

摘要：

智能时代已然来临。智能技术的发展正加速推动组织的智能化进程。越来越多的企业在生产和管理中采用智能技术以提高竞争力。在此背景下, 人机协同工作日益普遍, 人机协同决策成为新型组织决策方式。然而, 当前智能组织中的人机协同决策还面临信任度低、可控性低、透明度低、协同度低等一系列问题, 它们阻碍了决策质量、效率和体验的提升。本项目认为, 人机兼容性, 特别是人机内部兼容性, 如认知兼容性、情感兼容性、价值兼容性等, 或是影响人机协同决策绩效的根本原因。因此, 本项目基于人机内部兼容性理论视角, 综合采用决策心理学、认知科学、组织行为学等多学科理论与方法, 通过一系列现场研究和模拟实验, 力图揭示人机协同决策中存在的问题及其成因, 探究人机协同决策中内部兼容性的影响因素和作用机制, 进而提出若干人机协同决策优化方法。项目研究成果将有助于促进人机协同决策理论与人机兼容性理论的发展, 提升人机协同决策绩效, 推进组织决策的智能化进程。

关键词: 智能组织, 人机协同决策, 内部兼容性, 认知兼容性, 情感兼容性, 价值兼容性

Abstract:

The era of artificial intelligence has already arrived. With the rapid development of intelligent technology, more and more companies are adopting this technology into their business processes to enhance their core competitiveness. Subsequently, human-agent collaborative work is becoming common, and human-agent collaborative decision-making (HACDM) is evolving as a new form of organizational decision-making. However, evidence shows that HACDM still faces challenges, such as low trust and controllability toward agents, low transparency of agents, and low collaboration between humans and agents. Therefore, how these challenges can be overcome to improve the decision quality, decision efficiency, and user experience of HACDM is crucial to the field of organizational decision-making.
This project suggests that human-agent compatibility, especially human-agent inner compatibility (HAIC) which consists of cognitive, affective, and value compatibility, might be the fundamental factor affecting the performance of HACDM. Following the perspective of HAIC theory and using the multi-disciplinary methods from psychology, cognitive science, and organizational behavior, we intend to 1) reveal the existing problems within HACDM; 2) explore the impact of HAIC on the process and performance of HACDM; 3) propose methods to improve the performance of HACDM. Thus, this project consists of three studies. Study 1 aims to investigate real-world intelligent organizations to uncover the current usage of agents, the willingness of human employees and managers to collaborate with agents, and the possible problems within HACDM. Based on the findings of study 1, study 2 adopts HAIC theory as its framework and explores the influence of cognitive, affective, and value compatibilities on the process and performance of HACDM. Finally, study 3 tests the effectiveness of the several methods suggested by HAIC theory for improving HACDM, such as increasing the transparency of agents’ decisions and providing decision feedback to human employees.
This project’s findings will contribute both theoretically and practically. Theoretically, this project examines the components of HAIC (i.e., cognitive, affective, and value compatibilities) and investigates their influence on HACDM. Thus, it will contribute to the further development of human-agent compatibility theory and human-agent collaboration theory. Practically, the project proposes several methods that can effectively improve the performance of HACDM. Therefore, it will improve the performance of intelligent organizations and promote the intelligentization progress of HACDM.

Key words: intelligent organization, human-agent collaborative decision-making, human-agent inner compatibility, cognitive compatibility, affective compatibility, value compatibility

中图分类号:

B849

何贵兵, 陈诚, 何泽桐, 崔力丹, 陆嘉琦, 宣泓舟, 林琳. (2022). 智能组织中的人机协同决策：基于人机内部兼容性的研究探索. 心理科学进展 , 30(12), 2619-2627.

HE Guibing, CHEN Cheng, HE Zetong, CUI Lidan, LU Jiaqi, XUAN Hongzhou, LIN Lin. (2022). Human-agent collaborative decision-making in intelligent organizations: A perspective of human-agent inner compatibility. Advances in Psychological Science, 30(12), 2619-2627.

图/表 1

参考文献 48

[1]	陈婷, 孙晓敏. (2016). 团队决策中的共享信息偏差:基于隐藏文档范式的机制、影响因素探究. 心理科学进展, 24(1), 132-142.
[2]	管文颖. (2006). 团队决策中共享心理模型演化的影响因素研究 (硕士学位论文). 浙江大学, 杭州.
[3]	何贵兵. (2002). 群体动态决策的适应性行为及其内隐学习机制 (博士学位论文). 浙江大学, 杭州.
[4]	黄德斌. (2007). 团队决策中共享心理模型演化的STC机制研究 (硕士学位论文). 浙江大学, 杭州.
[5]	刘文川, 唐坚, 吴超. (2019). 人工智能与领导力: 基于复杂大数据支撑的政府决策机制研究. 改革与开放, 11, 42-46.
[6]	许为, 葛列众. (2020). 智能时代的工程心理学. 心理科学进展, 28(9), 1409-1425.
[7]	Bubb, H. (1993). Systemergonomie. In H. Schmidtke (Ed.), Ergonomie (pp. 333-420). München, Germany: CarlHanser.
[8]	Burton, J. W., Stein, M. K., & Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220-239. doi: 10.1002/bdm.2155
[9]	Cannon-Bowers, J. A., Salas, E., & Converse, S. (1993). Shared mental models in expert team decision making. In N. J. Castellan, Jr. (Ed.), Individual and group decision making: Current issues (pp.221-246). Lawrence Erlbaum Associates, Inc.
[10]	Chamorro-Premuzic, T., & Ahmetoglu, G. (2016). The pros and cons of robot managers. Harvard Business Review, 12, 2-5.
[11]	Christoffersen, K., & Woods, D. D. (2002). How to make automated systems team players. In E. Salas (Ed.), Advances in human performance and cognitive engineering research (Vol. 2, pp. 1-12). Bingley, UK: Emerald Group.
[12]	Coll, R., & Coll, J. H. (1989). Cognitive match interface design, a base concept for guiding the development of user friendly computer application packages. Journal of Medical Systems, 13(4), 227-235. pmid: 2592875
[13]	Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.
[14]	Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. doi: 10.1287/mnsc.35.8.982 URL
[15]	Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human- Computer Studies, 45(1), 19-45. doi: 10.1006/ijhc.1996.0040 URL
[16]	Demir, M., McNeese, N. J., & Cooke, N. J. (2020). Understanding human-robot teams in light of all-human teams: Aspects of team interaction and shared cognition. International Journal of Human-Computer Studies, 140, Article 102436. doi: 10.1016/j.ijhcs.2020.102436 URL
[17]	Dewhurst, M., & Willmott, P. (2014). Manager and machine: The new leadership equation. McKinsey Quarterly, 4(3), 76-86.
[18]	Dineen, B. R., Noe, R. A., & Wang, C. (2004). Perceived fairness of web-based applicant screening procedures: Weighing the rules of justice and the role of individual differences. Human Resource Management, 43(2-3), 127-145. doi: 10.1002/hrm.20011 URL
[19]	Dirks, K. T., & Ferrin, D. L. (2001). The role of trust in organizational settings. Organization Science, 12(4), 450-467. doi: 10.1287/orsc.12.4.450.10640 URL
[20]	Fan, X., & Yen, J. (2010). Modeling cognitive loads for evolving shared mental models in human-agent collaboration. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(2), 354-367. doi: 10.1109/TSMCB.2010.2053705 URL
[21]	Flemisch, F., Kelsch, J., Löper, C., Schieben, A., & Schindler, J. (2008). Automation spectrum, inner/outer compatibility and other potentially useful human factors concepts for assistance and automation. In D. de Waard, F. O. Flemisch, B. Lorenz, H. Oberheid, & K. A. Brookhuis (Eds.), Human Factors for assistance and automation (pp. 1-16). Maastricht, the Netherlands: Shaker Publishing.
[22]	Ghislieri, C., Molino, M., & Cortese, C. G. (2018). Work and Organizational Psychology Looks at the Fourth Industrial Revolution: How to Support Workers and Organizations? Frontiers in Psychology, 9, Article 2365. doi: 10.3389/fpsyg.2018.02365 pmid: 30546335
[23]	Hafenbrädl, S., Waeger, D., Marewski, J. N., & Gigerenzer, G. (2016). Applied decision making with fast-and-frugal heuristics. Journal of Applied Research in Memory and Cognition, 5(2), 215-231. doi: 10.1016/j.jarmac.2016.04.011 URL
[24]	Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53(5), 517-527. pmid: 22046724
[25]	Harms, P. D., & Han, G. (2019). Algorithmic leadership: The future is now. Journal of Leadership Studies, 12(4), 74-75.
[26]	Jonker, C., Van Riemsdijk, M., & Vermeulen, B. (2010, May). Shared Mental Models: A Conceptual Analysis. Proceedings of 9^th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada.
[27]	Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. doi: 10.1016/j.bushor.2018.08.004 URL
[28]	Lam, S. S., & Schaubroeck, J. (2011). Information sharing and group efficacy influences on communication and decision quality. Asia Pacific Journal of Management, 28(3), 509-528. doi: 10.1007/s10490-009-9183-y URL
[29]	Langer, M., König, C. J., Gebhard, P., & André, E. (2016). Dear computer, teach me manners: Testing virtual employment interview training. International Journal of Selection and Assessment, 24(4), 312-323. doi: 10.1111/ijsa.12150 URL
[30]	Larson, J. R., Christensen, C., Abbott, A. S., & Franz, T. M. (1996). Diagnosing groups: Charting the flow of information in medical decision-making teams. Journal of Personality and Social Psychology, 71(2), 315-330. pmid: 8765484
[31]	Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50-80. pmid: 15151155
[32]	Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5(1), 1-16.
[33]	Lim, B. C., & Klein, K. J. (2006). Team mental models and team performance: A field study of the effects of team mental model similarity and accuracy. Journal of Organizational Behavior, 27(4), 403-418. doi: 10.1002/job.387 URL
[34]	Lim, J. S., & O'Connor, M. (1996). Judgmental forecasting with interactive forecasting support systems. Decision Support Systems, 16(4), 339-357. doi: 10.1016/0167-9236(95)00009-7 URL
[35]	Madhavan, P., & Wiegmann, D. A. (2007). Similarities and differences between human-human and human-automation trust: An integrative review. Theoretical Issues in Ergonomics Science, 8(4), 277-301. doi: 10.1080/14639220500337708 URL
[36]	Mathieu, J. E., Heffner, T. S., Goodwin, G. F., Salas, E., & Cannon-Bowers, J. A. (2000). The influence of shared mental models on team process and performance. Journal of Applied Psychology, 85(2), 273-283. pmid: 10783543
[37]	Mittu, R., Sofge, D., Wagner, A., & Lawless, W. F. (2016). Robust intelligence and trust in autonomous systems. Boston, MA: Springer.
[38]	Mohammed, S., & Dumville, B. C. (2001). Team mental models in a team knowledge framework: Expanding theory and measurement across disciplinary boundaries. Journal of Organizational Behavior, 22(2), 89-106. doi: 10.1002/job.86 URL
[39]	Naim, I., Tanveer, M. I., Gildea, D., & Hoque, M. E. (2016). Automated analysis and prediction of job interview performance. IEEE Transactions on Affective Computing, 9(2), 191-204. doi: 10.1109/TAFFC.2016.2614299 URL
[40]	Ötting, S. K., & Maier, G. W. (2018). The importance of procedural justice in human-machine interactions: Intelligent systems as new decision agents in organizations. Computers in Human Behavior, 89, 27-39. doi: 10.1016/j.chb.2018.07.022 URL
[41]	Parry, K., Cohen, M., & Bhattacharya, S. (2016). Rise of the machines: A critical consideration of automated leadership decision making in organizations. Group and Organization Management, 41(5), 571-594. doi: 10.1177/1059601116643442 URL
[42]	Schaefer, K. E., Chen, J. Y. C., Szalma, J. L., & Hancock, P. A. (2016). A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human Factors, 58(3), 377-400. doi: 10.1177/0018720816634228 pmid: 27005902
[43]	Scheutz, M., DeLoach, S. A., & Adams, J. A. (2017). A framework for developing and using shared mental models in human-agent teams. Journal of Cognitive Engineering and Decision Making, 11(3), 203-224. doi: 10.1177/1555343416682891 URL
[44]	Sieck, W. R., & Arkes, H. R. (2005). The recalcitrance of overconfidence and its contribution to decision aid neglect. Journal of Behavioral Decision Making, 18(1), 29-53. doi: 10.1002/bdm.486 URL
[45]	Stasser, G., & Titus, W. (1985). Pooling of unshared information in group decision making: Biased information sampling during discussion. Journal of Personality and Social Psychology, 48(6), 1467-1478. doi: 10.1037/0022-3514.48.6.1467 URL
[46]	Stasser, G., & Titus, W. (1987). Effects of information load and percentage of shared information on the dissemination of unshared information during group discussion. Journal of Personality and Social Psychology, 53(1), 81-93. doi: 10.1037/0022-3514.53.1.81 URL
[47]	Wynne, K. T., & Lyons, J. B. (2018). An integrative model of autonomous agent teammate-likeness. Theoretical Issues in Ergonomics Science, 19(3), 353-374. doi: 10.1080/1463922X.2016.1260181 URL
[48]	Yokoi, R., & Nakayachi, K. (2021). The effect of value similarity on trust in the automation systems: A case of transportation and medical care. International Journal of Human-Computer Interaction, 37(13), 1269-1282. doi: 10.1080/10447318.2021.1876360 URL

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