Action in auctions: neural and computational mechanisms of bidding behaviour

Abstract

Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behaviour plays a major role in social competition. Yet, how humans learn to bid efficiently remains an open question. We used model‐based neuroimaging to investigate the neural mechanisms of bidding behaviour under different types of competition. Twenty‐seven subjects (nine male) played a prototypical bidding game: a double action, with three “market” types, which differed in the number of competitors. We compared different computational learning models of bidding: directional learning models (DL), where the model bid is “nudged” depending on whether it was accepted or rejected, along with standard reinforcement learning models (RL). We found that DL fit the behaviour best and resulted in higher payoffs. We found the binary learning signal associated with DL to be represented by neural activity in the striatum distinctly posterior to a weaker reward prediction error signal. We posited that DL is an efficient heuristic for valuation when the action (bid) space is continuous. Indeed, we found that the posterior parietal cortex represents the continuous action space of the task, and the frontopolar prefrontal cortex distinguishes among conditions of social competition. Based on our findings, we proposed a conceptual model that accounts for a sequence of processes that are required to perform successful and flexible bidding under different types of competition.

Neural mechanisms of expert persuasion on willingness to pay for sugar

“…The participants of the behavioral pilot study reported that 10 s was enough time for them to make a decision. The initial position of the marker on the WTP slider was randomized across trials (Martinez-Saito et al, 2019 ). The left and right keyboard keys allowed the participants to change the initial value of the slider to the value they wished, before pressing Enter key to confirm the bid.…”

Adaptive learning under strategic and structural uncertainty: the case of auction games

“…DL has been found to describe accurately a large body of experimental data (Mookherjee & Sopher, 1997;Erev & Rapoport, 1998;Erev & Roth, 1998a;Slonim & Roth, 1998;Camerer & Ho, 1999, Martinez-Saito et al, 2019, but it does not furnish testable quantitative predictions: the magnitude of this adjustment remains unspecified. This indeterminacy has hindered the development of theories of behavior in the spirit of DL, in search for parsimonious models that do not require the incorporation of ad hoc parameters .…”

“…Our market (double auction) experimental game was framed as the problem of a single trader (restaurant owner) who aims at buying a fixed-size commodity (fish) to be sold to his or her customers at a fixed price, and follows the design of a previous study (Martinez-Saito et al, 2019). The trader in each period comes to the market, where he faces one of the three possible market types: SC (two sellers, each having and willing to sell him one unit of good), NC (single seller with one unit of good), or BC (single seller but another buyer, competing with the trader for the same unit of good).…”