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Register a new userDo Informational Cascades Happen with Non-myopic Agents?
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We consider an environment where players need to decide whether to buy a certain product (or adopt a technology) or not. The product is either good or bad but its true value is not known to the players. Instead, each player has her own private information on its quality. Each player can observe the previous actions of other players and estimate the quality of the product. A classic result in the literature shows that in similar settings information cascades occur where learning stops for the whole network and players repeat the actions of their predecessors. In contrast to the existing literature on informational cascades, in this work, players get more than one opportunity to act. In each turn, a player is chosen uniformly at random and can decide to buy the product and leave the market or to wait. We provide a characterization of structured perfect Bayesian equilibria (sPBE) with forward-looking strategies through a fixed-point equation of dimensionality that grows only quadratically with the number of players. In particular, a sufficient state for players strategies at each time instance is a pair of two integers, the first corresponding to the estimated quality of the good and the second indicating the number of players that cannot offer additional information about the good to the rest of the players. Based on this characterization we study informational cascades in two regimes. First, we show that for a discount factor strictly smaller than one, informational cascades happen with high probability as the number of players increases. Furthermore, only a small portion of the total information in the system is revealed before a cascade occurs. Secondly, and more surprisingly, we show that for a fixed number of players, as the discount factor approaches one, bad informational cascades are benign when the product is bad, and are completely eliminated when the discount factor equals one.
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We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such settings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more qualified individuals are never (probabilistically) preferred over less qualified ones [Joseph et al]. We investigate whether it is possible to design inexpensive {subsidy} or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost $o(T)$ (for the classic setting with $k$ arms, $tilde{O}(sqrt{k^3T})$, and for the $d$-dimensional linear contextual setting $tilde{O}(dsqrt{k^3 T})$). If the principal has much more limited information (as might often be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the $k$ groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit settings by upper and lower bounding the cost of fair subsidy schemes.
We model sectoral production by cascading binary compounding processes. The sequence of processes is discovered in a self-similar hierarchical structure stylized in the economy-wide networks of production. Nested substitution elasticities and Hicks-neutral productivity growth are measured such that the general equilibrium feedbacks between all sectoral unit cost functions replicate the transformation of networks observed as a set of two temporally distant input-output coefficient matrices. We examine this system of unit cost functions to determine how idiosyncratic sectoral productivity shocks propagate into aggregate macroeconomic fluctuations in light of potential network transformation. Additionally, we study how sectoral productivity increments propagate into the dynamic general equilibrium, thereby allowing network transformation and ultimately producing social benefits.
Travellers in autonomous vehicles (AVs) need not to walk to the destination any more after parking like those in conventional human-driven vehicles (HVs). Instead, they can drop off directly at the destination and AVs can cruise for parking autonomously. It is a revolutionary change that such parking autonomy of AVs may increase the potential parking span substantially and affect the spatial parking equilibrium. Given this, from urban planners perspective, it is of great necessity to reconsider the planning of parking supply along the city. To this end, this paper is the first to examine the spatial parking equilibrium considering the mix of AVs and HVs with parking cruising effect. It is found that the equilibrium solution of travellers parking location choices can be biased due to the ignorance of cruising effects. On top of that, the optimal parking span of AVs at given parking supply should be no less than that at equilibrium. Besides, the optimal parking planning to minimize the total parking cost is also explored in a bi-level parking planning design problem (PPDP). While the optimal differentiated pricing allows the system to achieve optimal parking distribution, this study suggests that it is beneficial to encourage AVs to cruise further to park by reserving less than enough parking areas for AVs.
This paper studies reputation in the online market for illegal drugs in which no legal institutions exist to alleviate uncertainty. Trade takes place on platforms that offer rating systems for sellers, thereby providing an observable measure of reputation. The analysis exploits the fact that one of the two dominant platforms unexpectedly disappeared. Re-entering sellers reset their rating. The results show that on average prices decreased by up to 9% and that a 1% increase in rating causes a price increase of 1%. Ratings and prices recover after about three months. We calculate that identified good types earn 1,650 USD more per week.
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