Rewarding sequential innovators: prizes, patents and buyouts

This paper presents a model of cumulative innovation where firms are heterogeneous in their research ability. We study the optimal reward policy when the quality of the ideas and their subsequent development effort are private information. The optimal assignment of property rights must counterbalance the incentives of current and future innovators. The resulting mechanism resembles a menu of patents that have infinite duration and fixed scope, where the latter increases in the value of the idea. Finally, we provide a way to implement this patent menu by using a simple buyout scheme: The innovator commits at the outset to a price ceiling at which he will sell his rights to a future inventor. By paying a larger fee initially, a higher price ceiling is obtained. Any subsequent innovator must pay this price and purchase its own buyout fee contract.Patents

Rewarding Sequential Innovators: Patents Prizes and Buyouts

This paper presents a model of cumulative innovation where firms are heterogeneous in their research ability. We study the optimal reward policy when the quality of the ideas and their subsequent development effort are private information. The optimal assignment of property rights must counterbalance the incentives of current and future innovators. The resulting mechanism resembles a menu of patents that, contrary to the existing literature, have infinite duration and fixed scope, where the latter increases in the value of the idea. Finally, we provide a way to implement this patent menu by using a simple buyout scheme: The innovator commits at the outset to a price ceiling at which he will sell his rights to a future inventor. By paying a larger fee, a higher price ceiling is obtained. Any subsequent innovator must pay this price and purchase its own buyout fee contract.

Radiation-induced myeloid leukemia in murine models.

The use of radiation therapy is a cornerstone of modern cancer treatment. The number of patients that undergo radiation as a part of their therapy regimen is only increasing every year, but this does not come without cost. As this number increases, so too does the incidence of secondary, radiation-induced neoplasias, creating a need for therapeutic agents targeted specifically towards incidence reduction and treatment of these cancers. Development and efficacy testing of these agents requires not only extensive in vitro testing but also a set of reliable animal models to accurately recreate the complex situations of radiation-induced carcinogenesis. As radiation-induced leukemic progression often involves genomic changes such as rearrangements, deletions, and changes in methylation, the laboratory mouse Mus musculus, with its fully sequenced genome, is a powerful tool in cancer research. This fact, combined with the molecular and physiological similarities it shares with man and its small size and high rate of breeding in captivity, makes it the most relevant model to use in radiation-induced leukemia research. In this work, we review relevant M. musculus inbred and F1 hybrid animal models, as well as methods of induction of radiation-induced myeloid leukemia. Associated molecular pathologies are also included

Compensation and responsibility

This a chapter for the Handbook of Social Choice and Welfare. It deals with the theory of fairness applied to situations when individuals are partly responsible for their characteristics.fairness, responsibility, equal opportunity, compensation, handicap, talent, effort

REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes

One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales. To overcome this bottleneck, we present the REinforcement learning based Adaptive samPling (REAP) algorithm that aims to efficiently sample conformational space by learning the relative importance of each reaction coordinate as it samples the landscape. To achieve this, the algorithm uses concepts from the field of reinforcement learning, a subset of machine learning, which rewards sampling along important degrees of freedom and disregards others that do not facilitate exploration or exploitation. We demonstrate the effectiveness of REAP by comparing the sampling to long continuous MD simulations and least-counts adaptive sampling on two model landscapes (L-shaped and circular), and realistic systems such as alanine dipeptide and Src kinase. In all four systems, the REAP algorithm consistently demonstrates its ability to explore conformational space faster than the other two methods when comparing the expected values of the landscape discovered for a given amount of time. The key advantage of REAP is on-the-fly estimation of the importance of collective variables, which makes it particularly useful for systems with limited structural information

The future of securitization

Securitization is a financial innovation that experiences a boom-bust cycle, as many other innovations before. This paper analyzes possible reasons for the breakdown of primary and secondary securitization markets, and argues that misaligned incentives along the value chain are the primary cause of the problems. The illiquidity of asset and interbank markets, in this view, is a market failure derived from ill-designed mechanisms of coordinating financial intermediaries and investors. Thus, illiquidity is closely related to the design of the financial chains. Our policy conclusions emphasize crisis prevention rather than crisis management, and the objective is to restore a “comprehensive incentive alignment”. The toe-hold for strengthening regulation is surprisingly small. First, we emphasize the importance of equity piece retention for the long-term quality of the underlying asset pool. As a consequence, equity piece allocation needs to be publicly known, alleviating market pricing. Second, on a micro level, accountability of managers can be improved by compensation packages aiming at long term incentives, and penalizing policies with destabilizing effects on financial markets. Third, on a macro level, increased transparency relating to effective risk transfer, risk-related management compensation, and credible measurement of rating performance stabilizes the valuation of financial assets and, hence, improves the solvency of financial intermediaries. Fourth, financial intermediaries, whose risk is opaque, may be subjected to higher capital requirements