Daniel N Hauser's Website



Aalto University

School of Business

Department of Economics

P.O. Box 21210

FI-00076 AALTO

Email: daniel.hauser@aalto.fi

About Me:

I am a post doctoral researcher at Aalto University.

Research Interests:

Dynamic Games, Reputation, Social Learning, Model Misspecification


"Promoting a Reputation For Quality"

A firm manages its reputation not only by investing in the quality of its products, but also through promotional campaigns and other forms of advertisement. I model a firm who invests in both the quality of a product and in the information about quality it provides to the market. The market learns about the quality through information that the firm cannot influence and promotion controlled by the firm. When the market learns about quality primarily through promotion, the ability to promote creates and enhances incentives to invest in quality. This leads to reputation cycles, and periods of time where the firm promotes even though it is not investing in quality to increase the reputational dividend from past investment. Promotion impacts incentives for investment in quality, enhancing the incentives for investment at low reputations and eliminating equilibria with reputation traps, situations where low reputation firms can never reestablish a high reputation. In equilibrium the ability to promote also reduces incentives for investment at high reputations, leading to longer and larger reputation cycles than in environments with only exogenous news.

"Social Learning with Model Misspecification: A Framework and A Characterization," joint with Aislinn Bohren (R&R at Econometrica)

This paper explores model misspecification in the canonical observational learning framework. An agent's type specifies how he interprets private and public signals and how he believes others draw inference. This framework captures behavioral biases such as confirmation bias, underweighting or overweighting information, optimism bias and correlation neglect, as well as models of inference such as level-k and cognitive hierarchy. We develop a simple criterion to identify how behavioral biases in information processing and inference impact asymptotic learning. Depending on the nature of the bias, beliefs may converge to the incorrect state, the correct state, or not converge at all. Agents with different biases may hold different asymptotic beliefs, even though they are all observing the same sequence of prior actions. Next, we explore the optimal strategy of an information designer who can release costly public information and has a preference over the asymptotic beliefs of agents. The optimal timing, frequency and strength of public information depends crucially on the structure of model misspecification. This contrasts with the fully rational model, in which the timing, frequency and strength of information releases are irrelevant for asymptotic learning.

"Censorship and Reputation for Quality" (Draft available upon request)

I consider a model where a firm manages it's reputation both by investing in the quality of its products and by suppressing negative information about quality. This ability to censor bad news is a substitute for investment, instead of producing high quality products a firm could simply sell low quality products and hide any bad news. Sufficiently effective censorship technology has an extreme impact on incentives, completely eliminating any equilibria where the firm invests in the quality of its products.

Work in Progress

"Misinterpreting Social Outcomes and Information Campaigns," joint with Aislinn Bohren (Extended Abstract)

Given the different inefficiencies that arise when agents are misspecified, it is natural to ask what types of policies will improve decision-making. In this paper, we explore how information campaigns can counteract inefficient choices. We study the optimal way for a social planner to release costly public information about the state. For example, this could entail a public health campaign to encourage parents to vaccinate their children or a savings campaign that encourages workers to invest in the stock market. We show that the duration (temporary or permanent) and targeting (intervene to correct inefficient action choices or intervene to reinforce efficient action choices) of the optimal information campaign depends crucially on the form of misspecification.

"Social Learning with Endogenous Order of Moves" joint with Pauli Murto and Juuso Välimäki

We extend the canonical social learning model to allow for free timing of actions. A group of agents, each endowed with some private information, are trying to learn some unknown state of the world by observing the actions taken by other agents. Agents make a single irreversible decision but, unlike in the canonical model, they can choose to wait in order to observe what decisions others make. Previous literature has understated the role of this endogenous timing in facilitating information aggregation; we demonstrate that in the most informative symmetric equilibrium information fully aggregates as the number of players becomes large. In this limit, we also calculate the speed of learning, and show that allowing for endogenous timing introduces delays and periods of inactivity, endogenizing timing enables information aggregates at a faster rate than in many previously studied settings.