We study entrepreneurship in a setting where identical, loss-averse individuals choose between a risky entrepreneurial path and a safe outside option. The combination of effort and luck determine the single winner of the entrepreneurship market. We obtain a closed form solution to equilibrium entry and effort decisions. The novel implications of the model are: (1) Entrepreneurial effort increas...

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We explore combining the benefits of convolutional architectures and autoencoders for learning deep representations in an unsupervised manner. A major challenge is to achieve appropriate sparsity among hidden variables, since neighbouring variables in each feature map tend to be highly correlated and a suppression mechanism is therefore needed. Previously, deconvolutional networks and convoluti...

This paper presents two k-winners-take-all (k-WTA) networks with discontinuous activation functions. The k-WTA operation is first converted equivalently into linear and quadratic programming problems. Then two k-winners-take-all networks are designed based on the linear and quadratic programming formulations. The networks are theoretically guaranteed to be capable of performing the k-WTA operat...

"Winner-Take-All"-markets, i.e. markets in which the relative and not the absolute performance is decisive, have gained in importance. Such markets have a tendency to provoke inefficiently many entries. We investigate the functioning of such markets with the help of experiments and show that there are even more inefficient entries than predicted by the Nash equilibrium. Moreover, this effect in...

Recurrent networks that perform a winner-take-all computation have been studied extensively. Although some of these studies include spiking networks, they consider only analog input rates. We present results of this winner-take-all computation on a network of integrate-and-fire neurons which receives spike trains as inputs. We show how we can configure the connectivity in the network so that th...

DCPS (the Distributed Connectionist Production System) is a neural network with complex dynamical properties. Visualizing the energy landscapes of some of its component modules leads to a better intuitive understanding of the model, and suggests ways in which its dynamics can be controlled in order to improve performance on difficult cases.

This experiment compares the performance of two contest designs. In the winner-take-all tournament, the highest performing contestant wins a prize. In the proportional-payment design, that same prize is divided among the contestants according to their share of total achievement. We find that proportional prizes elicit higher entry rates and thus more total achievement than the winner-take-all t...

An analogue-VLSI winner-take-all circuit is enhanced through the addition of hysteretic feedback that emphasizes the spatial locality of competing signals using a resistive network. This circuit has application to tasks in areas such as image processing, in which inputs are not stationary with respect to the circuit array. Introduction Analogue VLSI circuits facilitate the implementation of low...