Farzad Pourbabaee

I received my PhD from the Department of Economics at UC Berkeley in spring 2021.
Currently, I am a postdoc at Caltech HSS division.
My research interests are in Economic Theory and Finance. Here is my CV.

Contact: far@caltech.edu or farzad@berkeley.edu

Research

    Working Papers:

  1. The Impact of Connectivity on the Production and Diffusion of Knowledge (with Gustavo Manso)
    PDF | arXiv | Presentation slides |

    Abstract. We study a social bandit problem featuring production and diffusion of knowledge. While higher connectivity enhances knowledge diffusion, it may reduce knowledge production as agents shy away from experimentation with new ideas and free ride on the observation of other agents. As a result, under some conditions, greater connectivity can lead to homogeneity and lower social welfare.

    (Future) presentations: NETSCIECON; Midwest Economic Theory; North American Summer Meetings of the Econometric Society 2022; Informs ADA 2022
  2. Reputation, Learning and Project Choice in Frictional Economies
    PDF | arXiv |

    Abstract. I introduce a dynamic model of learning and random meetings between a long-lived agent with unknown ability and heterogenous projects with observable types. There is incomplete yet symmetric information about the agent's ability. She needs to accept the contacting projects and create success to learn her type. Alternatively, lack of success during a match leads to a reputational loss followed from Bayesian learning, in that the reputation is interpreted as the posterior belief about the agent's ability. Developing a self-type learning framework with endogenous outside option, I find the optimal matching strategy of the agent, that determines what types of projects the agent with a certain level of reputation will accept. Comparing with a perfect information benchmark, I show learning incentives lead to larger matching sets in the optimum.

  3. Delegated Learning and Non-Credible Communication (with Peter B. McCrory)
    PDF | SSRN |

    Abstract. We consider a setting in which an impatient agent acquires payoff-relevant information about the true state of the world. The agent endogenously chooses when to stop learning, at which point an uninformed principal takes an action to maximize her own expected payoff. The agent's preferences are biased relative to the principal's, generating misalignment of expected payoffs. When communication is non-credible, the principal can only rely upon the agent's endogenous stopping rule when strategically specifying her course of action. In the no-communication equilibrium, the agent adopts a one-sided stopping rule as a function of her posterior belief that is consistent with the principal's pre-specified course of action at the time of stopping. When the principal has commitment power, relative to the full-communication equilibrium, the agent is always worse off; for intermediate values of prior beliefs, the principal is better off. The one-sided equilibrium stopping rule (and associated action) can switch discretely as a function of prior beliefs, generating dramatic regime changes for arbitrarily small changes in beliefs. When learning is initiated in the no-communication equilibrium there is a non-zero probability of indefinite delay, in which the agent never ceases learning and the principal never takes an action.

  4. Tail Probability Estimation of Factor Models with Regularly-Varying Tails: Asymptotics and Efficient Estimation (with Omid Sham Solari)
    PDF | SSRN |

    Abstract. We study the tail probability of linear factor models generated from non-identically distributed components with regularly-varying tails, a large subclass of heavy-tailed distributions. An efficient sampling method for tail probability estimation for this class is introduced and theoretically shown to exponentially outperform the crude Monte-Carlo estimator, in terms of the coverage probability and the confidence interval's length. The theoretical results are empirically validated through stochastic simulations on independent non-identically Pareto distributed factors. The proposed estimator is available as part of a more comprehensive TPE package.


  5. Publications:

  6. High Dimensional Decision Making, Upper and Lower Bounds
    Economics Letters, 2021.     PDF | Published Paper | arXiv |

    A decision maker's utility depends on her action \(a \in A \subset \mathbb{R}^d\) and the payoff relevant state of the world \(\theta \in \Theta\). One can define the value of acquiring new information as the difference between the maximum expected utility pre- and post information acquisition. In this paper, I find asymptotic results on the expected value of information as \(d \to \infty\), by using tools from the theory of (sub)-Guassian processes and generic chaining.

  7. Robust Experimentation in the Continuous Time Bandit Problem
    Economic Theory, 2020.     PDF | Published Paper | arXiv |

    Abstract. We study the experimentation dynamics of a decision maker (DM) in a two-armed bandit setup (Bolton and Harris [1999]), where the agent holds ambiguous beliefs regarding the distribution of the return process of one arm and is certain about the other one. The DM entertains Multiplier preferences á la Hansen and Sargent [2001], thus we frame the decision making environment as a two-player differential game against nature in continuous time. We characterize the DM's value function and her optimal experimentation strategy that turns out to follow a cut-off rule with respect to her belief process. The belief threshold for exploring the ambiguous arm is found in closed form and is shown to be increasing with respect to the ambiguity aversion index. We then study the effect of provision of an unambiguous information source about the ambiguous arm. Interestingly, we show that the exploration threshold rises unambiguously as a result of this new information source, thereby leading to more conservatism. This analysis also sheds light on the efficient time to reach for an expert opinion.

    First paragraph of the Intro ( Covid prophecy!) There are natural cases where the experimentation shall be performed in ambiguous environments, where the distribution of future shocks is unknown. For example, consider a diagnostician who has two treatments for a particular set of symptoms. One is the conventional treatment that has been widely tested and has a known success rate. Alternatively, there is a second treatment that is recently discovered and is due to further study. The diagnostician shall perform a sequence of experiments on patients to figure out the success/failure rate of the new treatment. However, the adversarial effects of the mistreatment on certain types of patients are fatal, thus the medics must consider the worst case scenario on the patients while evaluating the new treatment.

  8. Risk Minimization and Portfolio Diversification (with Minsuk Kwak and Traian A. Pirvu)
    Quantitative Finance, 2016.     PDF | Published Paper | arXiv
  9. Lattice Coding for Multiple Access Channels with Common Message and Additive Interference
    Information Theory Workshop (IEEE), 2012.     Published Paper

Teaching

Caltech HSS:

First year PhD courses (UC Berkeley):

Undergraduate courses (UC Berkeley):