People compete harder against humans than against AI opponents
People escalate against a competitor labelled human and hold back against one labelled an optimising machine
Human-Computer Interaction
Summary
People change how hard they compete based on whether they think their opponent is human or a machine. When told they might face a human, they tend to compete more aggressively, driving up costs. But when told the opponent is a strong AI optimising to win, they hold back. The authors separated effects of information from actual opponent type in an experiment with auctions, finding that simply labeling an opponent affects behaviour independently of who they really face.
What this means in practice
- •For competition designers: Design contests or auctions to account for how competitor labels influence participant effort and bidding behaviour.
- •For policy makers: Craft policies on AI-human competition narratives knowing descriptions impact participant behaviours independently of actual AI performance.
Authors
Vinicius Ferraz, Leon Houf
Abstract
People increasingly compete against AI agents rather than other human opponents. We distinguish two channels: an opponent effect and an information effect. These are different elements with different consequences: the opponent effect is specific to a given computational system, the information effect a property of the information environment that an organisation or policymaker can control. We separate them in a preregistered experiment (N = 1,395) using a dynamic all-pay auction, a repeated contest in which escalation of commitment arises from the incentives. What participants are told about the opponent (human, an AI trained to imitate people, or an AI trained to compete well) is varied and crossed with who they actually face, in a deception-free design. What people are told influences escalation: the median price rises by 6.7 points when a human might be the opponent and falls by 8.8 when an optimising machine might be, a spread of about 15% of the prize value of the competition, produced by information alone. Competing against the AI agents lowers prices, yet reduces the chance that both sides finish with positive earnings, showing distinct effects of the opponent channel. The information effect is not explained by articulated strategy, or individual differences, and is consistent with a competitive response engaged when a human is a live possibility. This shows that describing an AI competitor is not behaviourally neutral.