Wei Li
Research Area
About
I became deeply interested in the interaction of information and incentives in various economics and political environment during my master studies at Harvard University. I continued to pursue this topic at MIT, where I obtained my Ph.D.
My research fields consist of contract theory, applied game theory, and information economics. The overriding theme of my research is how the presence of asymmetric information affects people’s incentive to communicate truthfully, why many commonly-observed channels of communication exist as they do, and how we should design communication protocols to best adapt to these incentives.
In my spare time, I enjoy reading, yoga and arts in all its glorious forms.
Teaching
Research
Please click on paper titles for abstracts and full text downloads.
We present a new framework in which agents with limited and heterogeneous cognitive ability—modeled as finite depths of reasoning—learn from their neighbors in social networks. Each agent tracks old information using Bayes-like formulas, and uses a shortcut when reasoning on behalf of multiple neighbors exceeds her cognitive ability. Surprisingly, agents with moderate cognitive ability are capable of partialing out old information and learn correctly in social quilts, a tree-like union of cliques (fully-connected subnetworks). Agents with low cognitive ability may fail to learn in any network, even when they receive a large number of signals. We also identify a critical cutoff level of cognitive ability, determined by the network structure, above which an agent’s learning outcome remains the same even when her cognitive ability increases.
Agents in a network want to learn the true state of the world from their own signals and their neighbors’ reports. Agents know only their local networks, consisting of their neighbors and the links among them. Every agent is Bayesian with the (possibly misspecified) prior belief that her local network is the entire network.We present a tractable learning rule to implement such locally Bayesian learning:each agent extracts new information using the full history of observed reports in her local network. Despite their limited network knowledge, agents learn correctly when the network is a social quilt, a tree-like union of cliques. But they fail to learn when a network contains interlinked circles (echo chambers), despite an arbitrarily large number of correct signals.
Experts often collect and report information over time. What reporting protocol elicits the most information? Here, a principal receives reports sequentially from an agent with privately known ability, who observes two signals about the state of the world. The signals differ in initial quality and, unlike previous work, differ in quality improvement. The paper finds that “mind changes” (inconsistent reports) can signal talent if a smart agent improves faster. Also, sequential reports dominate when the principal’s decision is very sensitive to information; a single report dominates if the mediocre agent’s signals improve faster, or the agent is likely mediocre.
We analyze ways in which heterogeneity in responsiveness to incentives (“drive”) affects employees’ incentives and firms’ incentive systems in a career concerns model. On the one hand, because more driven agents work harder in response to existing incentives than less driven ones—and therefore pay is increasing in perceived drive—there is a motive to increase effort to signal high drive. These “drive-signaling incentives” are strongest with intermediate levels of existing incentives. On the other hand, because past output of a more driven agent will seem to the principal to reflect lower ability, there is an incentive to decrease effort to signal low drive. The former effect dominates early in the career, and the latter effect dominates towards the end. To maximize incentives, the principal wants to observe a noisy measure of the agent’s effort—such as the number of hours he works—early but not late in his career.
A sender may communicate with a decision maker through intermediaries. In this model, an objective sender and intermediary pass on information truthfully, while biased ones favor a particular agenda but also have reputational concerns. I show that the biased sender and the biased intermediary’s reporting truthfulness are strategic complements. The biased sender is less likely to use an intermediary than an objective sender is if his reputational concerns are low; but more likely to do so if his reputational concerns are moderate. Moreover, the biased sender may be more likely to use an intermediary perceived to be more biased.
An agent’s productivity depends on his responsiveness to existing incentives (“drive”). Over the long term, this heterogeneity in drive may create significant incentives for the agent to work hard even with vanishingly small amount of existing incentives, explicit or implicit.
A privately informed sender may influence the decision maker through an intermediary who is better informed than him. I assume that the objective sender and intermediary pass on their best information, while the biased ones prefer a particular action but also have reputational concerns. I show that the biased intermediary selectively incorporates the sender’s information to push his agenda, and his truth-tellingincentives always decrease in those of the biased sender. Hence measures making it more costly for the sender to lie worsen the biased intermediary’s distortion, and may make the decision maker strictly worse off.
A candidate for political office has private information about his and his rival’s qualifications. A more informative positive (negative) campaign generates a more accurate public signal about his own (his rival’s) qualifications, but costs more. A high type candidate has a comparative advantage in negative campaigns if, relative to the low type, he can lower the voter’s belief about his rival more effectively than he can raise her belief about himself and vice versa. In equilibrium, this comparative advantage determines whether the high type chooses a positive or negative campaign. Further, competition helps the high type separate.