Seller Reputation and Trust in Pre-Trade Communication

We characterize the unique equilibrium in which high ability sellers always announce the quality of their items truthfully, in a repeated game model of experienced good markets with adverse selection on a seller's propensity to supply good quality items. In this equilibrium a seller's value function strictly increases in reputation and a seller's type is revealed within finite time. The analysis highlights a new reputation mechanism based on an endogenous complementarity the market places between a seller's honesty in pre-trade communication (trust) and his/her ability to deliver good quality (reputation). As maintaining honesty is less costly for high ability sellers who anticipate less “bad news” to disclose, they can signal their ability by communicating in a more trustworthy manner. Applying this model, we examine the extent to which consumer feedback systems foster trust in online markets, including the possibility that sellers may change identities or exit.cheap talk, consumer rating system, reputation, trust.

Transparency and sensorimotor contingencies: Do we see through photographs?

It has been claimed that photographs are transparent: we see through them; we literally see the photographed object through the photograph. Whether this claim is true depends on the way we conceive of seeing. There has been a controversy about whether localizing the perceived object in one's egocentric space is a necessary feature of seeing, as if it is, then photographs are unlikely to be transparent. I would like to propose and defend another, much weaker, necessary condition for seeing: I argue that it is necessary for seeing that there is at least one way for me to move such that if I were to move this way, my view of the perceived object would change continuously as I move. Since this condition is not satisfied in the case of seeing objects in photographs, photographs are not transparen

Spartan Daily, May 14, 1993

Volume 100, Issue 68https://scholarworks.sjsu.edu/spartandaily/8426/thumbnail.jp

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication