A general framework is suggested to describe human decision making in a certain class of experiments performed in a trading laboratory. We are in particular interested in discerning between two different moods, or states of the investors, corresponding to investors using fundamental investment...

We propose a nouvel methodology for forecasting chaotic systems which uses information on local Lyapunov exponents (LLEs) to improve upon existing predictors by correcting for their inevitable bias. Using simulations of the Rössler, Lorenz and Chua attractors, we find that accuracy gains can be...

We propose a novel methodology for forecasting chaotic systems which is based on exploiting the information conveyed by the local Lyapunov ex- ponent of a system. We show how our methodology can improve forecast- ing within the attractor and illustrate our results on the Lorenz system.

Avec son objectif affiché d'éliminer la pauvreté en France, l' « allocation personnelle » explore une nouvelle approche institutionnelle de l'assistance. Pour mieux saisir en quoi cette proposition rompt avec les pratiques existantes ou en débat, ce texte vise à situer le dispositif de l'...

Using French data, we show that ELIE performs rather weakly when it comes to addressing the issue of poverty. Yet, eliminating poverty is also a valid normative property of any redistribution mechanism. We suggest combining ELIE with another redistributive solution aimed specifically at...

We propose a novel methodology for forecasting chaotic systems which is based on the nearest-neighbor predictor and improves upon it by incorporating local Lyapunov exponents to correct for its inevitable bias. Using simulated data, we show that gains in prediction accuracy can be substantial....

We observe that a Pigovian climate policy need not exact full payment of the social cost of carbon upon emission to yield optimal incentives. Following this insight, we propose the creation of a carbon liabilities market to address climate change. Each period, countries would be made liable for...