Platform Competition under Asymmetric Information

In the context of platform competition in a two-sided market, we study how exante uncertainty and ex-post asymmetric information concerning the value of a new technology aects the strategies of the platforms and the market outcome. We nd that the incumbent dominates the market by setting the welfare-maximizing quantity when the dierence in the degree of asymmetric information between buyers and sellers is signicant. However, if this dierence is below a certain threshold, then even the incumbent platform will distort its quantity downward. Since a monopoly incumbent would set the welfare-maximizing quantity, this result indicates that platform competition may lead to a market failure: Competition results in a lower quantity and lower welfare than a monopoly. We consider two applications of the model. First, we consider multi-homing. We nd that multi-homing solves the market failure resulting from asymmetric information. However, if platforms can impose exclusive dealing, then they will do so, which result in market ineciency. Second, the model provides a new argument for why it is usually entrants, not incumbents, that bring major technological innovations to the market

Dynamic competition with network externalities: why history matters

We consider dynamic competition among platforms in a market with network externalities. A platform that dominated the market in the previous period be- comes \focal" in the current period, in that agents play the equilibrium in which they join the focal platform whenever such equilibrium exists. Yet when faced with higher-quality competition, can a low-quality platform remain focal? In the nite-horizon case, the unique equilibrium is ecient for \patient" platforms; with an innite time horizon, however, there are multiple equilibria where ei- ther the low- or high-quality platform dominates. If qualities are stochastic, the platform with a better average quality wins with a higher probability, even when its realized quality is lower, and this probability increases as platforms become more patient. Hence social welfare may decline as platforms become more forward looking

Dynamic competition with network externalities: why history matters

We consider dynamic competition among platforms in a market with network externalities. A platform that dominated the market in the previous period be- comes \focal" in the current period, in that agents play the equilibrium in which they join the focal platform whenever such equilibrium exists. Yet when faced with higher-quality competition, can a low-quality platform remain focal? In the nite-horizon case, the unique equilibrium is ecient for \patient" platforms; with an innite time horizon, however, there are multiple equilibria where ei- ther the low- or high-quality platform dominates. If qualities are stochastic, the platform with a better average quality wins with a higher probability, even when its realized quality is lower, and this probability increases as platforms become more patient. Hence social welfare may decline as platforms become more forward looking

Platform Competition under Asymmetric Information

In the context of platform competition in a two-sided market, we study how exante uncertainty and ex-post asymmetric information concerning the value of a new technology aects the strategies of the platforms and the market outcome. We nd that the incumbent dominates the market by setting the welfare-maximizing quantity when the dierence in the degree of asymmetric information between buyers and sellers is signicant. However, if this dierence is below a certain threshold, then even the incumbent platform will distort its quantity downward. Since a monopoly incumbent would set the welfare-maximizing quantity, this result indicates that platform competition may lead to a market failure: Competition results in a lower quantity and lower welfare than a monopoly. We consider two applications of the model. First, we consider multi-homing. We nd that multi-homing solves the market failure resulting from asymmetric information. However, if platforms can impose exclusive dealing, then they will do so, which result in market ineciency. Second, the model provides a new argument for why it is usually entrants, not incumbents, that bring major technological innovations to the market

Dynamic competition with network externalities: how history matters

We consider dynamic competition among platforms in a market with network externalities. A platform that dominated the market in the previous period becomes “focal” in the current period, in that agents play the equilibrium in which they join the focal platform whenever such equilibrium exists. Yet when faced with higher‐quality competition, can a low‐quality platform remain focal? In the finite‐horizon case, the unique equilibrium is efficient for “patient” platforms; with an infinite time horizon, however, there are multiple equilibria where either the low‐ or high‐quality platform dominates. If qualities are stochastic, the platform with a better average quality wins with a higher probability, even when its realized quality is lower, and this probability increases as platforms become more patient. Hence, social welfare may decline as platforms become more forward looking

Correcting a bias in the computation of behavioural time budgets that are based on supervised learning

Abstract: Supervised learning of behavioural modes from body acceleration data has become a widely used research tool in Behavioural Ecology over the past decade. One of the primary usages of this tool is to estimate behavioural time budgets from the distribution of behaviours as predicted by the model. These serve as the key parameters to test predictions about the variation in animal behaviour. In this paper we show that the widespread computation of behavioural time budgets is biased, due to ignoring the classification model confusion probabilities. Next, we introduce the confusion matrix correction for time budgets—a simple correction method for adjusting the computed time budgets based on the model's confusion matrix. Finally, we show that the proposed correction is able to eliminate the bias, both theoretically and empirically in a series of data simulations on body acceleration data of a fossorial rodent species (Damaraland mole‐rat Fukomys damarensis). Our paper provides a simple implementation of the confusion matrix correction for time budgets, and we encourage researchers to use it to improve accuracy of behavioural time budget calculations