Abstract:

I study self-learning pricing algorithms and show that they are collusive in market simulations. To derive a counterfactual that resembles traditional tacit collusion, I conduct market experiments with humans in the same environment. Across different treatments, I vary the market size and the number of firms that use a pricing algorithm. I demonstrate that oligopoly markets can become more collusive if algorithms make pricing decisions instead of humans. In two-firm markets, prices are weakly increasing in the number of algorithms in the market. In three-firm markets, algorithms weaken competition if most firms use an algorithm and human sellers are inexperienced.

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Abstract:

Volunteering is a widespread allocation mechanism in the workplace. It emerges naturally in software development or the generation of online knowledge platforms. Using a field experiment with more than 2000 workers, we study the effect of team size on volunteering in an online labor market. In contrast to our theoretical predictions and previous research, we find no effect of team size on volunteering, although workers react to free-riding incentives. We replicate the results and provide further robustness checks. Eliciting workers’ beliefs about their co-workers’ volunteering reveals conditional volunteering as the primary driver of our results. 

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Abstract:

Algorithms play an increasingly important role in economic situations. Often these situations are strategic, where the artificial intelligence may or may not be cooperative. We study the determinants and forms of algorithmic cooperation in the infinitely repeated prisoner's dilemma. We run a sequence of computational experiments, accompanied by additional repeated prisoner's dilemma games played by humans in the lab. We find that the same factors that increase human cooperation largely also determine the cooperation rates of algorithms. However, algorithms tend to play different strategies than humans. Algorithms cooperate less than humans when cooperation is very risky or not incentive compatible.

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Abstract:

This paper investigates the collusive and competitive effects of algorithmic price recommendations on market outcomes. These recommendations are often non-binding and common in many markets, especially on online platforms. We develop a theoretical framework and derive two algorithms that recommend collusive pricing strategies. Utilizing a laboratory experiment, we find that sellers condition their prices on the recommendation of the algorithms. The algorithm with a soft punishment strategy lowers market prices and has a pro-competitive effect. The algorithm that recommends a subgame perfect equilibrium strategy increases the range of market outcomes, including more collusive ones. Variations in economic preferences lead to heterogeneous treatment effects and explain the results.

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Abstract:

The market for video games is booming, with in-game purchases accounting for a substantial share of developers' revenues. Policymakers and the general public alike are concerned that so-called loot boxes - lotteries that offer random rewards to be used in-game - induce consumers to overspend on video games. We provide experimental evidence suggesting that common design features of loot boxes (such as opaque odds and positively selected feedback) indeed induce overspending by inflating the belief of winning a prize. In combination, these features double the average willingness-to-pay for lotteries. Based on our findings, we argue for the need to regulate the design of loot boxes to protect consumers from overspending.

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Work in progress