On whom would I want to depend; humans or computers?

Abstract

We study in a laboratory experiment whether humans prefer to depend on decisions of others (Human-Driven Uncertainty) or states generated by a computer (Computerized Uncertainty). The experimental design introduced in this paper is unique in that it introduces Human-Driven Uncertainty such that it does not derive from a strategic context. In our experiment, Human-Driven Uncertainty derives from decisions, which were taken in a morally neutral context and in ignorance of externalities that the decisions may have on others. Our results indicate that even without strategic interaction and moral elements humans prefer Computerized to Human-Driven Uncertainty. This holds even when the distribution of outcomes under both types of uncertainty is identical. From a methodological point of view, the findings shed a critical light on behavioral research in which it is common practice to control for strategic uncertainty by comparing interaction with an artificial agent with a known strategy to interaction with humans. Outside the laboratory, our results suggest that whenever dependence on humans is changed to dependence on computers and other kinds of "artificial" decision makers, preferences with regard to these dependencies may change too.

Does risk communication really decrease cooperation in climate change mitigation?

“…While (ii) is uncertainty as it is studied in the literature on individual decision making, (i) is often referred to as strategic uncertainty. Evidence on how decisions and preferences in individual and strategic settings relate to each other has been inconclusive and greatly dependent on the exact type of strategic setting considered (Bohnet et al 2008;Fairley et al 2016;Farjam 2015). It also remains unclear whether residual risk is perceived differently in individual and strategic contexts.…”

Algorithms in selection decisions: Effective, but unappreciated

“…In fact, they found evidence for “algorithmic appreciation,” where individuals preferred algorithmic advice over human advice, including their own judgments, when compared directly. The inclination to favor algorithms was observed in several “objective” areas, such as logic problems (Dijkstra et al, 1998), word association task (Bogert et al, 2022), assessing the magnitude of visual stimuli (Logg et al, 2019), predicting product demand (Daschner & Obermaier, 2022), and choosing lottery numbers (Farjam, 2019). It was also evident in taste‐related contexts, such as ranking the popularity of songs, predicting romantic attraction (Logg et al, 2019), and divulging personal information and feelings (Lucas et al, 2014).…”

Measuring strategic-uncertainty attitudes

“…In contrast, we elicit certainty equivalents for each potential action in the game without the stated certainty equivalents affecting payoffs in the game. 5 A comparison of risk and ambiguity driven either by human behavior or computer is proposed by Farjam (2019). However, he focuses on non-strategic human-driven uncertainty and shows that computerized uncertainty is preferred.…”