This paper considers the financing of a research project under uncertainty about the time of completion and the probability of eventual success. The uncertainty about future success gradually diminishes with the arrival of addtional funding. The entrepreneur controls the funds and can divert them. We distinguish between relationship financing, meaning that the entrepreneur’s allocation of the funds is observable, and arm’s length financing, where it is unobservable.
We find that equilibrium funding stops altogether too early relative to the efficient stopping time in both financing modes. We characterize the optimal contracts and equilibrium funding decisions. The financial constraints will typically become tighter over time under relationship finance, and looser under arm’s length financing. The trade-off is that while relationship financing may require smaller information rents, arm’s length financing amounts to an implicit commitment to a finite funding horizon. The lack of such a commitment under relationship financing implies that the sustainable release of funds eventually slows down. We obtain the surprising result that arm’s length contracts are preferable in a Pareto sense.
This paper considers the financing of a research project under uncertainty about the time of completion and the probability of eventual success. The uncertainty about future success gradually diminishes with the arrival of addtional funding. The entrepreneur controls the funds and can divert them. We distinguish between relationship financing, meaning that the entrepreneur’s allocation of the funds is observable, and arm’s length financing, where it is unobservable.
We find that equilibrium funding stops altogether too early relative to the efficient stopping time in both financing modes. We characterize the optimal contracts and equilibrium funding decisions. The financial constraints will typically become tighter over time under relationship finance, and looser under arm’s length financing. The trade-off is that while relationship financing may require smaller information rents, arm’s length financing amounts to an implicit commitment to a finite funding horizon. The lack of such a commitment under relationship financing implies that the sustainable release of funds eventually slows down. We obtain the surprising result that arm’s length contracts are preferable in a Pareto sense.
This paper analyzes the entry of new products into vertically differentiated markets where an entrant and an incumbent compete in quantities. The value of the new product is initially uncertain and new information is generated through purchases in the market. We derive the (unique) Markov perfect equilibrium of the infinite horizon game under the strong long run average payoff criterion.
The qualitative features of the optimal entry strategy are shown to depend exclusively on the relative ranking of established and new products based on current beliefs. Superior products are launched relatively slowly and at high initial prices whereas substitutes for existing products are launched aggressively at low initial prices.
The robustness of these results with respect to different model specifications is discussed.
We consider a general mechanism design setting where each agent can acquire (covert) information before participating in the mechanism. The central question is whether a mechanism exists which provides the efficient incentives for information acquisition ex-ante and implements the efficient allocation conditional on the private information ex-post.
It is shown that in every private value environment the Vickrey-Groves-Clark mechanism guarantees both ex-ante as well as ex-post efficiency. In contract, with common values, ex-ante and ex-post efficiency cannot be reconciled in general. Sufficient conditions in terms of sub- and supermodularity are provided when (all) ex-post efficient mechanisms lead to private under- or over-acquisition of information.
A model of repeated price competition with large buyers is analyzed. The sellers are allowed to offer different prices to different buyers and the buyers act strategically. The set of subgame perfect Equilibria is investigated under public and private monitoring.
With public monitoring the equilibrium set with large buyers expands relative to the standard model where each buyer is small and behaves myopically.
With private monitoring, where prices are not observable to the competing sellers, the set of equilibrium payoffs shrinks. In the finitely repeated game with private monitoring, all sales are made by the efficient seller. In the infinitely repeated game this result is preserved as long as the sellers condition their prices on the public history. In contrast to the finite horizon game, the set of pure strategy equilibria expands if the sellers are allowed to condition their own past prices. Comparisons are drawn to Markovian equilibria of similar dynamic games.
This note shows that the optimal choice of k simultaneous experiments in a stationary multi-armed bandit problem can be characterized in terms of the Gittins index of each arm. The index characterization remains equally valid after the introduction of switching costs.
This note shows that the optimal choice of k simultaneous experiments in a stationary multi-armed bandit problem can be characterized in terms of the Gittins index of each arm. The index characterization remains equally valid after the introduction of switching costs.
A model of repeated price competition with large buyers is analyzed. The sellers are allowed to offer different prices to different buyers and the buyers act strategically. The set of subgame perfect Equilibria is investigated under public and private monitoring.
With public monitoring the equilibrium set with large buyers expands relative to the standard model where each buyer is small and behaves myopically.
With private monitoring, where prices are not observable to the competing sellers, the set of equilibrium payoffs shrinks. In the finitely repeated game with private monitoring, all sales are made by the efficient seller. In the infinitely repeated game this result is preserved as long as the sellers condition their prices on the public history. In contrast to the finite horizon game, the set of pure strategy equilibria expands if the sellers are allowed to condition their own past prices. Comparisons are drawn to Markovian equilibria of similar dynamic games.
This paper analyzes the optimal entry into experience goods markets with vertically differentiated buyers. We consider the case where the value of the new product is imperfectly known, but common to all buyers (common values) as well as the case where the quality is different across buyers (private values).
We distinguish between new products that are improvements to existing products and new products that are substitutes. Different types of products have qualitatively distinct diffusion paths. Improvements are introduced slowly relative to the full information case, while substitutes are introduced more aggressively. The slow entry strategy is associated with increasing supply and decreasing prices over time. The reverse pattern holds for an aggressive entry strategy
The incentives to innovate display a similar distinction. A firm with a currently inferior product opts for a large but risky innovation, whereas a currently superior producer chooses a smaller but certain innovation.
We present a model of entry and exit with Bayesian learning and price competition. A new product of initially unknown quality is introduced in the market, and purchases of the product yield information on its true quality. We assume that the performance of the new product is publicly observable. As agents learn from the experiments of others, informational externalities arise.
We determine the Markov Perfect Equilibrium prices and allocations. In a single market, the combination of the informational externalities among the buyers and the strategic pricing by the sellers results in excessive experimentation. If the new product is launched in many distinct markets, the path of sales converges to the efficient path in the limit as the number of markets grows.
We present a model of entry and exit with Bayesian learning and price competition. A new product of initially unknown quality is introduced in the market, and purchases of the product yield information on its true quality. We assume that the performance of the new product is publicly observable. As agents learn from the experiments of others, informational externalities arise.
We determine the Markov Perfect Equilibrium prices and allocations. In a single market, the combination of the informational externalities among the buyers and the strategic pricing by the sellers results in excessive experimentation. If the new product is launched in many distinct markets, the path of sales converges to the efficient path in the limit as the number of markets grows.
We consider a general model of dynamic common agency with symmetric information. We focus on Markov perfect equilibria and characterize the equilibrium set for a refinement of the Markov perfect equilibria.
Particular attention is given to the existence of a marginal contribution equilibrium where each principal receives her contribution to the coalition of agent and remaining principals. The structure of the intertemporal payoffs is analyzed in terms of the flow marginal contribution. As a byproduct, new results for the static common agency game are obtained.
The general characterization results are then applied to two dynamic bidding games for a common agent: (i) multi-task allocation and (ii) job matching under uncertainty.
We consider a general model of dynamic common agency with symmetric information. We focus on Markov perfect equilibria and characterize the equilibrium set for a refinement of the Markov perfect equilibria.
Particular attention is given to the existence of a marginal contribution equilibrium where each principal receives her contribution to the coalition of agent and remaining principals. The structure of the intertemporal payoffs is analyzed in terms of the flow marginal contribution. As a byproduct, new results for the static common agency game are obtained.
The general characterization results are then applied to two dynamic bidding games for a common agent: (i) multi-task allocation and (ii) job matching under uncertainty.