Incentives for experimenting agents

We examine a repeated interaction between an agent who undertakes experiments and a principal who provides the requisite funding. A dynamic agency cost arises—the more lucrative the agent's stream of rents following a failure, the more costly are current incentives, giving the principal a motivation to reduce the project's continuation value. We characterize the set of recursive Markov equilibria. Efficient equilibria front‐load the agent's effort, inducing maximum experimentation over an initial period, until switching to the worst possible continuation equilibrium. The initial phase concentrates effort near the beginning, when most valuable, whereas the switch attenuates the dynamic agency cost.     

On the existence of shadow prices

For utility maximization problems under proportional transaction costs, it has been observed that the original market with transaction costs can sometimes be replaced by a frictionless shadow market that yields the same optimal strategy and utility. However, the question of whether or not this indeed holds in generality has remained elusive so far. In this paper, we present a counterexample which shows that shadow prices may fail to exist. On the other hand, we prove that short selling constraints are a sufficient condition to warrant their existence, even in very general multi-currency market models with possibly discontinuous bid–ask spreads.     

Techno-ökonomische Bewertung von Energie-Autarkie für die Energieversorgung von Einfamilienhäusern

Many consumers currently follow the idea of energy self-sufficiency and try to contribute to meet their energy needs in order to become independent and self-sufficient from the central power supply system. In order to achieve load-oriented energy self-sufficiency the provision of energy must cover the full energy demand at any time. Against this background, in this paper the costs and potentials of a load-oriented energy self-sufficiency of single-family homes are analysed. Thereby it is differentiated between electricity-, heat- and energy self-sufficiency. The modelling is carried out with the simulation environment ?Polysun Designer“ which allows a high temporal dynamic simulation of the annual energy demand and supply.The results show that, within the investigated supply variations, the highest levels of energy self-sufficiency can be achieved by an energy supply system completely based on electricity using a combination of PV; heat pump and battery storage. Depending on the building standard, a maximum of 45 (existing buildings) and 71?% (new buildings) of the building’s energy demand can be covered with renewable energy. The economic evaluation however has shown that under present conditions, none of the investigated supply variants can compete with conventional energy supply (public grid connection + gas condensing boiler).     

Obfuscation and Rational Inattention*

We study the behavior of duopolistic firms that can obfuscate their prices before competing on price. Obfuscation affects the rational inattentive consumers' optimal information strategy, which determines the probabilistic demand. Our model advances related models by allowing consumers to update their unrestricted prior beliefs with an informative signal of any form. We show that the game may result in an obfuscation equilibrium with high prices or a transparency equilibrium with low prices and no obfuscation, providing an argument for market regulation. Obfuscation equilibria cease to exist for low information costs and if one firm seems a priori considerably more attractive.     

Limits of semistatic trading strategies

We show that pointwise limits of semistatic trading strategies in discrete time are again semistatic strategies. The analysis is carried out in full generality for a two-period model, and under a probabilistic condition for multiperiod, multistock models. Our result contrasts with a counterexample of Acciaio, Larsson, and Schachermayer, and shows that their observation is due to a failure of integrability rather than instability of the semistatic form. Mathematically, our results relate to the decomposability of functions as studied in the context of Schrödinger bridges.     

Do traders learn to select efficient market institutions?

Experimental Economics - When alternative market institutions are available, traders have to decide both where and how much to trade. We conducted an experiment where traders decided first whether...     

Subsidizing unit donations: matches,rebates, and discounts compared

Experimental Economics - An influential result in the literature on charitable giving is that matching subsidies dominate rebate subsidies in raising funds. We investigate whether this result...     

Effects of agri-environment schemes on farm-level eco-efficiency measures: Empirical evidence from EU countries

The European Union (EU) budget allocated to agri-environmental schemes (AES) has increased consistently over the past 20 years. European citizens should expect effective spending of these public funds, so investigation of the effects of these schemes on both environmental benefits and farm efficiency is warranted. We examine the effects of European agri-environmental schemes on farm-level eco-efficiency. Our analysis combines data envelopment analysis and impact assessment methods to evaluate the impact of scheme payments on eco-efficiency measures. Our results suggest that there is considerable scope for eco-efficiency improvements, both for dairy and crop production. Results also show that the average change in eco-efficiency scores does not vary significantly between AES participants and non-participants, which questions the effectiveness of present AES.     

Detecting insurance fraud using supervised and unsupervised machine learning

Fraud is a significant issue for insurance companies, generating much interest in machine learning solutions. Although supervised learning for insurance fraud detection has long been a research focus, unsupervised learning has rarely been studied in this context, and there remains insufficient evidence to guide the choice between these branches of machine learning for insurance fraud detection. Accordingly, this study evaluates supervised and unsupervised learning using proprietary insurance claim data. Furthermore, we conduct a field experiment in cooperation with an insurance company to investigate the performance of each approach in terms of identifying new fraudulent claims. We derive several important findings. Unsupervised learning, especially isolation forests, can successfully detect insurance fraud. Supervised learning also performs strongly, despite few labeled fraud cases. Interestingly, unsupervised and supervised learning detect new fraudulent claims based on different input information. Therefore, for implementation, we suggest understanding supervised and unsupervised methods as complements rather than substitutes.