1,786 research outputs found
LONGRUN TRENDS IN NEW ZEALAND INDUSTRY ASSISTANCE
New Zealand has had a complex process of adjusting commercial policy since the economic reforms began in 1984. This paper reports on the results of a research project to estimate the trends in industry assistance over this period and the variance of assistance within 2- digit industry groupings.commercial policy, industry assistance, tariffs, New Zealand
Optimistic Agents are Asymptotically Optimal
We use optimism to introduce generic asymptotically optimal reinforcement
learning agents. They achieve, with an arbitrary finite or compact class of
environments, asymptotically optimal behavior. Furthermore, in the finite
deterministic case we provide finite error bounds.Comment: 13 LaTeX page
General time consistent discounting
Modeling inter-temporal choice is a key problem in both computer science and economic theory. The discounted utility model of Samuelson is currently the most popular model for measuring the global utility of a time-series of local utilities. The model is limited by not allowing the discount function to change with the age of the agent. This is despite the fact that many agents, in particular humans, are best modelled with age-dependent discount functions. It is well known that discounting can lead to time-inconsistent behaviour where agents change their preferences over time. In this paper we generalise the discounted utility model to allow age-dependent discount functions. We then extend previous work in time-inconsistency to our new setting, including a complete characterisation of time-(in)consistent discount functions, the existence of sub-game perfect equilibrium policies where the discount function is time-inconsistent and a continuity result showing that “nearly” time-consistent discount rates lead to “nearly” time-consistent behaviour
No Free Lunch versus Occam's Razor in Supervised Learning
The No Free Lunch theorems are often used to argue that domain specific
knowledge is required to design successful algorithms. We use algorithmic
information theory to argue the case for a universal bias allowing an algorithm
to succeed in all interesting problem domains. Additionally, we give a new
algorithm for off-line classification, inspired by Solomonoff induction, with
good performance on all structured problems under reasonable assumptions. This
includes a proof of the efficacy of the well-known heuristic of randomly
selecting training data in the hope of reducing misclassification rates.Comment: 16 LaTeX pages, 1 figur
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