Opportunity Search in the Era of GenAI: Navigating Uncertainty in an Expanding Universe of Imaginable but Unknowable Futures

社会学 创造力 生成语法 期货合约 稀缺 创业 认识论 过程(计算) 经济 方案规划 不确定性(哲学) 启发式 人类智力 满意选择 管理科学 即兴创作 独创性 情报分析 隐性知识 宇宙 人工智能 环境伦理学 实证经济学 计算机科学 对话的自我 模棱两可 知识管理 宣言 透视图(图形) 空格(标点符号) 卓越 未来研究 元理论 认知科学 生成模型 矛盾心理 管理
作者
Stratos Ramoglou,Yanto Chandra,Qian Jin
出处
期刊:Journal of Management Studies [Wiley]
卷期号:63 (2): 695-721 被引量:3
标识
DOI:10.1111/joms.70011
摘要

Abstract Entrepreneurship has often been viewed through a lens of scarcity of creativity. Yet, the arrival of generative artificial intelligence (GenAI) forces us to appreciate that the bottleneck of entrepreneurship is not the lack of creative ideas but Knightian Uncertainty. In an era of abundant entrepreneurial ideas, what matters is whether AI‐generated entrepreneurial futures are possible or figments of machine imagination. However, extant theory offers little guidance on navigating opportunity uncertainty – let alone amid an ever‐expanding universe of AI‐generated ideas that increases the risk of unsustainable venturing. Addressing what we theorize as a “grand epistemological challenge”, we develop a model of intelligent opportunity search. The architecture of the model is informed by Gerd Gigerenzer’s paradigm shift in decision‐making under uncertainty, centred on the use of heuristics that match the structure of the environment. Our model advances a symbiotic division of epistemic labour between machine and human intelligence guided by decision strategies attuned to the structure of the decision environment as reshaped in the GenAI era. The gist of the model is that machine creativity expands the ideation space through generative variation, while human judgment contracts it through a curation process geared towards the elimination of non‐opportunities. This structured opportunity detection process reflects a new ecology of entrepreneurial action, where successful opportunity search depends less on human creativity and imagination and more on eliminating what cannot be actualized. Besides advancing a novel perspective on the nature of human and machine symbiosis, this paper unpacks implications for opportunity theory and Knightian Uncertainty.
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