Both metabolism and behavior play a key role in biological theory and artificial life modelling. Yet, despite their centrality there has been very little exploration of the relationship between these concepts and almost no exploration of how the interaction between the two could impact on evolution

This paper argues that a broad class of search models cannot generate the observed business-cycle-frequency fluctuations in unemployment and job vacancies in response to shocks of a plausible magnitude. In the U.S., the vacancy-unemployment ratio is 20 times as volatile as average labor productivity, while under weak assumptions, search models predict that the vacancy-unemployment ratio and labor productivity have nearly the same variance. I establish this claim both using analytical comparative statics in a very general deterministic search model and using simulations of a stochastic version of the model. I show that a shock that changes average labor productivity primarily alters the present value of wages, generating only a small movement along a downward sloping Beveridge curve (unemployment-vacancy locus). A shock to the job destruction rate generates a counterfactually positive correlation between unemployment and vacancies. In both cases, the shock is only slightly amplified and the model exhibits virtually no propagation. I reconcile these findings with an existing literature and argue that the source of the model’s failure is lack of wage rigidity, a consequence of the assumption that wages are determined by Nash bargaining. ∗ This is a major revision of ‘Equilibrium Unemployment Fluctuations’. I thank Daron Acemoglu, Olivier

Current standard security practices do not provide substantial assurance that the end-to-end behavior of a computing system satisfies important security policies such as confidentiality. An end-to-end confidentiality policy might assert that secret input data cannot be inferred by an attacker

, and the morphology and behavior can adapt to each other as they evolve simultaneously. The genotypes are structured as directed graphs of nodes and connections, and they can efficiently but flexibly describe instructions for the development of creatures ’ bodies and control systems with repeating or recursive

of abuse suggest the evolution of higher mammals to support more sophistithat midbrain dopamine systems are involved in processing reward cated forms of individual and social behavior. Thus biologiinformation and learning approach behavior. Most dopamine neucal and cognitive needs define the nature

and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: • highly reliable communication whenever and wherever needed; • efficient utilization of the radio spectrum. Following

Most of a signal information is often found in irregular structures and transient phenomena. We review the mathematical characterization of singularities with Lipschitz exponents. The main theorems that estimate local Lipschitz exponents of functions, from the evolution across scales

, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.

In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning