9,143 research outputs found
Dundee Discussion Papers in Economics 154:Labour demand in Germany: an assessment on non-wage labour
Dundee Discussion Papers in Economics 220:China's new labour contract law: no harm to employment?
In January 2008, China imposed a new labour contract law. This new law is the most significant reform to the law of employment relations in mainland China in more than a decade. The paper provides a theoretical framework on the inter-linkages between labour market regulation, option value and the choice and timing of employment. All in all, the paper demonstrates that the Labour Contract Law in it´s own right will have only small impacts upon employment in the fast-growing Chinese economy. On the contrary, induced increasing unit labour costs represent the real issue and may reduce employment
Risk-Sensitive Reinforcement Learning: A Constrained Optimization Viewpoint
The classic objective in a reinforcement learning (RL) problem is to find a
policy that minimizes, in expectation, a long-run objective such as the
infinite-horizon discounted or long-run average cost. In many practical
applications, optimizing the expected value alone is not sufficient, and it may
be necessary to include a risk measure in the optimization process, either as
the objective or as a constraint. Various risk measures have been proposed in
the literature, e.g., mean-variance tradeoff, exponential utility, the
percentile performance, value at risk, conditional value at risk, prospect
theory and its later enhancement, cumulative prospect theory. In this article,
we focus on the combination of risk criteria and reinforcement learning in a
constrained optimization framework, i.e., a setting where the goal to find a
policy that optimizes the usual objective of infinite-horizon
discounted/average cost, while ensuring that an explicit risk constraint is
satisfied. We introduce the risk-constrained RL framework, cover popular risk
measures based on variance, conditional value-at-risk and cumulative prospect
theory, and present a template for a risk-sensitive RL algorithm. We survey
some of our recent work on this topic, covering problems encompassing
discounted cost, average cost, and stochastic shortest path settings, together
with the aforementioned risk measures in a constrained framework. This
non-exhaustive survey is aimed at giving a flavor of the challenges involved in
solving a risk-sensitive RL problem, and outlining some potential future
research directions
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