Economics

We develop a theory of repeated interaction for coalitional behavior. We consider stage games where both individuals and coalitions may deviate. However, coalition members cannot commit to long-run behavior, and anticipate that today's actions influence tomorrow's behavior. We evaluate the degree to which history-dependence can deter coalitional deviations. If monitoring is perfect, every feasible and strictly individually rational payoff can be supported by history-dependent conventions. By contrast, if players can make secret side-payments to each other, every coalition achieves a coalitional minmax value, potentially reducing the set of supportable payoffs to the core of the stage game.

In this paper we propose a mechanism for the allocation of pipeline capacities, assuming that the participants bidding for capacities do have subjective evaluation of various network routes. The proposed mechanism is based on the concept of bidding for route-quantity pairs. Each participant defines a limited number of routes and places multiple bids, corresponding to various quantities, on each of these routes. The proposed mechanism assigns a convex combination of the submitted bids to each participant, thus its called convex combinatorial auction. The capacity payments in the proposed model are determined according to the Vickrey-Clarke-Groves principle. We compare the efficiency of the proposed algorithm with a simplified model of the method currently used for pipeline capacity allocation in the EU (simultaneous ascending clock auction of pipeline capacities) via simulation, according to various measures, such as resulting utility of players, utilization of network capacities, total income of the auctioneer and fairness.

If agents cooperate only within small groups of some bounded sizes, is there a way to partition the population into small groups such that no collection of agents can do better by forming a new group? This paper revisited f-core in a transferable utility setting. By providing a new formulation to the problem, we built up a link between f-core and the transportation theory. Such a link helps us to establish an exact existence result, and a characterization result of f-core for a general class of agents, as well as some improvements in computing the f-core in the finite type case.

The core is a traditional and useful solution concept in economic theory. But in discrete exchange economies without transfers, when endowments are complex, the core may be empty. This motivates Balbuzanov and Kotowski (2019) to interpret endowments as exclusion rights and propose a new concept called exclusion core. Our contribution is twofold. First, we propose a rectification of the core to solve its problem under complex endowments. Second, we propose a refinement of Balbuzanov and Kotowski's exclusion core to improve its performance. Our two core concepts share a common idea of correcting the misused altruism of unaffected agents in blocking coalitions. We propose a mechanism to find allocations in the two cores.

Pandemic response is a complex affair. Most governments employ a set of quasi-standard measures to fight COVID-19 including wearing masks, social distancing, virus testing and contact tracing. We argue that some non-trivial factors behind the varying effectiveness of these measures are selfish decision-making and the differing national implementations of the response mechanism. In this paper, through simple games, we show the effect of individual incentives on the decisions made with respect to wearing masks and social distancing, and how these may result in a sub-optimal outcome. We also demonstrate the responsibility of national authorities in designing these games properly regarding the chosen policies and their influence on the preferred outcome. We promote a mechanism design approach: it is in the best interest of every government to carefully balance social good and response costs when implementing their respective pandemic response mechanism.

This paper corrects some mathematical errors in Holmström (1999) and clarifies the assumptions that are sufficient for the results of Holmström (1999). The results remain qualitatively the same.

We study how a principal should optimally choose between implementing a new policy and maintaining the status quo when information relevant for the decision is privately held by agents. Agents are strategic in revealing their information; the principal cannot use monetary transfers to elicit this information, but can verify an agent's claim at a cost. We characterize the mechanism that maximizes the expected utility of the principal. This mechanism can be implemented as a cardinal voting rule, in which agents can either cast a baseline vote, indicating only whether they are in favor of the new policy, or they make specific claims about their type. The principal gives more weight to specific claims and verifies a claim whenever it is decisive.

We present a limits-to-arbitrage model to study the impact of securitization, leverage and credit risk protection on the cyclicity of bank credit. In a stable bank credit situation, no cycles of credit expansion or contraction appear. Unlevered securitization together with mis-pricing of securitized assets increases lending cyclicality, favoring credit booms and busts. Leverage changes the state of affairs with respect to the simple securitization. First, the volume of real activity and banking profits increases. Second, banks sell securities when markets decline. This selling puts further pressure on falling prices. The mis-pricing of credit risk protection or securitized assets influences the real economy. Trading in these contracts reduces the amount of funding available to entrepreneurs, particularly to high-credit-risk borrowers. This trading decreases the liquidity of the securitized assets, and especially those based on investments with high credit risk.

In this paper, the credit scoring problem is studied by incorporating networked information, where the advantages of such incorporation are investigated theoretically in two scenarios. Firstly, a Bayesian optimal filter is proposed to provide risk prediction for lenders assuming that published credit scores are estimated merely from structured financial data. Such prediction can then be used as a monitoring indicator for the risk management in lenders' future decisions. Secondly, a recursive Bayes estimator is further proposed to improve the precision of credit scoring by incorporating the dynamic interaction topology of clients. It is shown that under the proposed evolution framework, the designed estimator has a higher precision than any efficient estimator, and the mean square errors are strictly smaller than the Cramér-Rao lower bound for clients within a certain range of scores. Finally, simulation results for a special case illustrate the feasibility and effectiveness of the proposed algorithms.

We study a cheap-talk game where two experts first choose what information to acquire and then offer advice to a decision-maker whose actions affect the welfare of all. The experts cannot commit to reporting strategies. Yet, we show that the decision-maker's ability to cross-verify the experts' advice acts as a commitment device for the experts. We prove the existence of an equilibrium, where an expert's equilibrium payoff is equal to what he would obtain if he could commit to truthfully revealing his information.