Learning and excess volatility

We introduce adaptive learning behavior into a general equilibrium lifecycle economy with capital accumulation. Agents form forecasts of the rate of return to capital assets using least squares autoregressions on past data. We show that, in contrast to the perfect foresight dynamics, the dynamical system under learning possesses equilibria characterized by persistent excess volatility in returns to capital. We explore a quantitative case for these learning equilibria. We use an evolutionary search algorithm to calibrate a version of the system under learning and show that this system can generate data that matches some features of the time series data for U.S. stock returns and per capita consumption. We argue that this finding provides support for the hypothesis that the observed excess volatility of asset returns can by explained by changes in investor expectations against a background of relatively small changes in fundamental factors.Capital ; Stock - Prices

Road Pricing with Autonomous Links

This research examines road pricing on a network of autonomous highway links. By autonomous it is meant that the links are competitive and independent, with the objective of maximizing their own profits without regard for either social welfare or the profits of other links. The principal goal of the research is to understand the implications of adoption of road pricing and privatization on social welfare and the distribution of gains and losses. The specific pricing strategies of autonomous links are evaluated first under the condition of competition for simple networks. An agent-based modeling system is developed which integrates an equilibrated travel demand, route choice, and travel time model with a repeated game of autonomous links setting prices to maximize profit. The levels of profit, welfare consequences, and potential cooperative arrangements undertaken by autonomous links will be evaluated. By studying how such an economic system may behave under various circumstances, the effectiveness of road pricing and road privatization as public policy can be assessed.Network dynamics, road pricing, autonomous links, privatization, agent-based transportation model

Prospects for large-scale financial systems simulation

As the 21st century unfolds, we find ourselves having to control, support, manage or otherwise cope with large-scale complex adaptive systems to an extent that is unprecedented in human history. Whether we are concerned with issues of food security, infrastructural resilience, climate change, health care, web science, security, or financial stability, we face problems that combine scale, connectivity, adaptive dynamics, and criticality. Complex systems simulation is emerging as the key scientific tool for dealing with such complex adaptive systems. Although a relatively new paradigm, it is one that has already established a track record in fields as varied as ecology (Grimm and Railsback, 2005), transport (Nagel et al., 1999), neuroscience (Markram, 2006), and ICT (Bullock and Cliff, 2004). In this report, we consider the application of simulation methodologies to financial systems, assessing the prospects for continued progress in this line of research

Numerical Simulation of Nonoptimal Dynamic Equilibrium Models

In this paper we present a recursive method for the computation of dynamic competitive equilibria in models with heterogeneous agents and market frictions. This method is based upon a convergent operator over an expanded set of state variables. The fixed point of this operator defines the set of all Markovian equilibria. We study approximation properties of the operator as well as the convergence of the moments of simulated sample paths. We apply our numerical algorithm to two growth models, an overlapping generations economy with money, and an asset pricing model with financial frictions.Heterogeneous agents, taxes, externalities, financial frictions, competitive equilibrium, computation, simulation

Spatial competition of learning agents in agricultural procurement markets

Spatially dispersed farmers supply raw milk as the primary input to a small number of large dairy-processing firms. The spatial competition of processing firms has short- to long-term repercussions on farm and processor structure, as it determines the regional demand for raw milk and the resulting raw milk price. A number of recent analytical and empirical contributions in the literature analyse the spatial price competition of processing firms in milk markets. Agent-based models (ABMs) serve by now as computational laboratories in many social science and interdisciplinary fields and are recently also introduced as bottom-up approaches to help understand market outcomes emerging from autonomously deciding and interacting agents. Despite ABMs' strengths, the inclusion of interactive learning by intelligent agents is not sufficiently matured. Although the literature of multi-agent systems (MASs) and multi-agent economic simulation are related fields of research they have progressed along separate paths. This thesis takes us through some basic steps involved in developing a theoretical basis for designing multi-agent learning in spatial economic ABMs. Each of the three main chapters of the thesis investigates a core issue for designing interactive learning systems with the overarching aim of better understanding the emergence of pricing behaviour in real, spatial agricultural markets. An important problem in the competitive spatial economics literature is the lack of a rigorous theoretical explanation for observed collusive behavior in oligopsonistic markets. The first main chapter theoretically derives how the incorporation of foresight in agents' pricing policy in spatial markets might move the system towards cooperative Nash equilibria. It is shown that a basic level of foresight invites competing firms to cease limitless price wars. Introducing the concept of an outside option into the agents' decisions within a dynamic pricing game reveals viihow decreasing returns for increasing strategic thinking correlates with the relevance of transportation costs. In the second main chapter, we introduce a new learning algorithm for rational agents using H-PHC (hierarchical policy hill climbing) in spatial markets. While MASs algorithms are typically just applicable to small problems, we show experimentally how a community of multiple rational agents is able to overcome the coordination problem in a variety of spatial (and non-spatial) market games of rich decision spaces with modest computational effort. The theoretical explanation of emerging price equilibria in spatial markets is much disputed in the literature. The majority of papers attribute the pricing behavior of processing firms (mill price and freight absorption) merely to the spatial structure of markets. Based on a computational approach with interactive learning agents in two-dimensional space, the third main chapter suggests that associating the extent of freight absorption just with the factor space can be ambiguous. In addition, the pricing behavior of agricultural processors – namely the ability to coordinate and achieve mutually beneficial outcomes - also depends on their ability to learn from each other

Market equilibria and money

By the first welfare theorem, competitive market equilibria belong to the core and hence are Pareto optimal. Letting money be a commodity, this paper turns these two inclusions around. More precisely, by generalizing the second welfare theorem we show that the said solutions may coincide as a common fixed point for one and the same system. Mathematical arguments invoke conjugation, convolution, and generalized gradients. Convexity is merely needed via subdifferentiablity of aggregate “cost”, and at one point only. Economic arguments hinge on idealized market mechanisms. Construed as algorithms, each stops, and a steady state prevails if and only if price-taking markets clear and value added is nil.publishedVersio

Classifying Monetary Economics: Fields and Methods from Past to Future

We propose a simple, yet sufficiently encompassing classification scheme of monetary economics. It comprises three fundamental fields and six recent areas that expand within and across these fields. The elements of our scheme are not found together and in their mutual relationships in earlier studies of the relevant literature, neither is this an attempt to produce a relatively complete systematization. Our intention in taking stock is not finality or exhaustiveness. We rather suggest a viewpoint and a possible ordering of the accumulating knowledge. Our hope is to stimulate an improved understanding of the evolving nature and internal consistency of monetary economics at large.monetary economics, monetary theory, monetary policy, public finance, classification, methodology

Three implications of learning behaviour for price processes.

no abstract availableConsumers' preferences; Economics -- Psychological aspects;