This paper studies the endogenous formation of supply chain networks when procurement is subject to disruption risk. We argue that the presence of non-convexities in the chain (e.g., due to non-convex production technologies or financial constraints) may create a wedge in the sourcing incentives of firms at different tiers, leading to the formation of overly fragile supply chains. More specifically, we show that even though upstream firms may find it optimal to employ multi-sourcing strategies as a way of mitigating supply disruption risks, such strategies lead to a more intertwined supply chain, which may exacerbate the extent of risk propagation further downstream: multi-sourcing by upstream firms may increase the likelihood of simultaneous disruptions in all procurement channels available to the downstream firms. We establish that under fairly general conditions, the losses due to such system-wide disruptions outweigh the benefits of multi-sourcing, thus, implying that the endogenously formed supply networks may be excessively interconnected.
Collaboration within a group of agents working towards the same goal is often hindered by incentives to free ride on effort provision. Agents are less likely to exert costly effort to generate information that is beneficial for the group. We are interested in providing qualitative insights on information sharing among the agents affects their free-riding incentives and subsequently the group's performance. In particular, we consider a setting in which agents experiment with an opportunity of unknown value. Information generated by an agent's experimentation is not directly observable to others but can be credibly disclosed to the rest of the group. We show that there is an optimal time T>0 such that if agents commit not to share any information up to T and fully disclose all available information at T, then the incentives to free ride are weakened and (part of) the group's efficiency is restored. Our analysis suggests that for creative problem-solving within organizations a hybrid structure, in which individuals first work independently and then combine their efforts, is superior to the team structure, where the group works together from the onset.
This paper studies sourcing decisions of firms in a multi-tiered supply chain when procurement is subject to disruption risk. We argue that the presence of non-convexities in the chain (e.g., due to non-convex production technologies or financial constraints) may create a wedge in the sourcing incentives of firms at different tiers, leading to the formation of inefficient supply chains. More specifically, we provide a characterization of whether and how non-convexities in upstream firms' production technologies may induce them to adopt sourcing strategies that are sub-optimal from the points of view of firms further downstream. Our analysis highlights that strategies focusing on optimizing procurement decisions in each supply chain tier in isolation may not be sufficient for mitigating risks at an aggregate level. Rather, a thorough understanding of the entire structure of the supply network is necessary to properly assess and secure against disruptive events.
Risk pooling has been extensively studied in the operations management literature as the basic driver behind strategies such as transshipment, manufacturing flexibility, component commonality, and drop-shipping. This paper explores the benefits of pooling in the context of inventory management using the canonical model first studied in Eppen(1979). Specifically, we consider a single-period multi-location newsvendor model, where different locations face independent and identically distributed demands and linear holding and backorder costs. We show that Eppen's celebrated result, i.e., that the cost savings from centralized inventory management scale with the square root of the number of locations, depends critically on the "light-tailed" nature of the demand uncertainty. In particular, we establish that the relative benefits of risk pooling for a class of heavy-tailed demand distributions (stable distributions) scale as n(α-1)/α, i.e., lower than √n predicted for normally distributed demands, where α ∈ (1,2] is a parameter that captures the shape of the distribution's tail. Furthermore, we discuss the implications of our findings for the performance of periodic-review policies in multi-period inventory management as well as for the profits associated with drop-shipping fulfillment strategies. Paired with an extensive simulation analysis, these results highlight the importance of taking into account the shape of the tail of the demand uncertainty when considering implementing a risk-pooling strategy.
Recent advances in information technology have allowed firms to gather vast amounts of data regarding consumers' preferences and the structure and intensity of their social interactions. This paper examines a game-theoretic model of competition between firms, which can target their marketing budgets to individuals embedded in a social network. We provide a sharp characterization of the optimal targeted marketing strategies and highlight their dependence on the underlying social network structure. Furthermore, we identify network structures for which the returns to targeting are maximized, and we provide conditions under which it is optimal for the firms to asymmetrically target a subset of the individuals. Finally, we provide a lower bound on the extent of asymmetry in these asymmetric equilibria and therefore shed light on the effect of the network structure to the outcome of marketing competition between firms.
This paper argues that in the presence of liquidation costs, portfolio diversification by financial institutions may be socially inefficient. We propose a stylized model in which individual banks have an incentive to diversify their risks. Yet, at the same time, diversification may increase the aggregate risk faced by the banks' depositors, creating a negative externality. The increase in systemic risk is due to the fact that even though diversification decreases the probability of each bank's failure, it may increase the probability of joint failures, which may be socially inefficient when the depositors are risk-averse. The presence of such externalities suggests that financial innovations that enable banks to engineer more diversified portfolios have non-trivial welfare implications.
We develop a model of information exchange through communication and investigate its implications for information aggregation in large societies. An underlying state determines payoffs from different actions. Agents decide which others to form a costly communication link with incurring the associated cost. After receiving a private signal correlated with the underlying state, they exchange information over the induced communication network until taking an (irreversible) action. We define asymptotic learning as the fraction of agents taking the correct action converging to one as a society grows large.
Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the induced communication network most agents are a short distance away from information hubs, which receive and distribute a large amount of information. Asymptotic learning therefore requires information to be aggregated in the hands of a few agents. We also show that while truthful communication may not always be a best response, it is an equilibrium when the communication network induces asymptotic learning. Moreover, we contrast equilibrium behavior with a socially optimal strategy profile, i.e., a profile that maximizes aggregate welfare. We show that when the network induces asymptotic learning, equilibrium behavior leads to maximum aggregate welfare, but this may not be the case when asymptotic learning does not occur.
We then provide a systematic investigation of what types of cost structures and associated social cliques (consisting of groups of individuals linked to each other at zero cost, such as friendship networks) ensure the emergence of communication networks that lead to asymptotic learning. Our result shows that societies with too many and sufficiently large social cliques do not induce asymptotic learning, because each social clique would have sufficient information by itself, making communication with others relatively unattractive. Asymptotic learning results either if social cliques are not too large, in which case communication across cliques is encouraged, or if there exist very large cliques that act as information hubs.
We study the optimal pricing strategies of a monopolist selling a divisible good (service) to consumers that are embedded in a social network. A key feature of our model is that consumers experience a (positive) local network effect. In particular, each consumer's usage level depends directly on the usage of her neighbors in the social network structure. Thus, the monopolist's optimal pricing strategy may involve offering discounts to certain agents, who have a central position in the underlying network.
Our results can be summarized as follows. First, we consider a setting where the monopolist can offer individualized prices and derive an explicit characterization of the optimal price for each consumer as a function of her network position. In particular, we show that it is optimal for the monopolist to charge each agent a price that is proportional to her Bonacich centrality in the social network. In the second part of the paper, we discuss the optimal strategy of a monopolist that can only choose a single uniform price for the good and derive an algorithm polynomial in the number of agents to compute such a price.
Thirdly, we assume that the monopolist can offer the good in two prices, full and discounted, and study the problem of determining which set of consumers should be given the discount. We show that the problem is NP-hard, however we provide an explicit characterization of the set of agents that should be offered the discounted price. Next, we describe an approximation algorithm for finding the optimal set of agents. We show that if the profit is nonnegative under any feasible price allocation, the algorithm guarantees at least 88% of the optimal profit. Finally, we highlight the value of network information by comparing the profits of a monopolist that does not take into account the network effects when choosing her pricing policy to those of a monopolist that uses this information optimally.
This paper studies a simple model of experimentation and innovation. Our analysis suggests that patents improve the allocation of resources by encouraging rapid experimentation and efficient ex post transfer of knowledge. Each firm receives a signal on the success probability of a project and decides when to experiment. Successes can be copied. First, we assume that signal qualities are the same. Symmetric equilibria involve delayed and staggered experimentation, whereas the optimal allocation never involves delays and may involve simultaneous experimentation. Appropriately designed patents implement the optimal allocation. Finally, we discuss the case when signals differ and are private information.
We study the efficiency of oligopoly equilibria in a model where firms compete over capacities and prices. Our model economy corresponds to a two-stage game. First, firms choose their capacity levels. Second, after the capacity levels are observed, they set prices. Given the capacities and prices, consumers allocate their demands across the firms. We establish the existence of pure strategy oligopoly equilibria and characterize the set of equilibria. We then investigate the efficiency properties of these equilibria, where "efficiency" is defined as the ratio of surplus in equilibrium relative to the first best. We show that efficiency in the worst oligopoly equilibria can be arbitrarily low. However, if the best oligopoly equilibrium is selected (among multiple equilibria), the worst-case efficiency loss is 2(√N-1)/(N-1) with N firms, and this bound is tight. We also suggest a simple way of implementing the best oligopoly equilibrium.
Conference PapersCournot Competition in Networked Markets, with S Ehsani and R Ilkilic, EC 2014
Optimal Pricing in
the Presence of Local Network
Effects, with O Candogan and A
Ozdaglar, WINE 2010
Networks, with D
Acemoglu and A Ozdaglar, Allerton,
Learning in Social Networks:
Partial Results, with
D Acemoglu and A Ozdaglar, Allerton,
Competition with Atomic Users, with A Ozdaglar, Asilomar, 2007
Partial Results on Capacity Competition, with D Acemoglu and A Ozdaglar, Allerton, 2006