A rational route to randomness
01 Jan 2004-pp 166-202
TL;DR: In this paper, the most important papers in the area of complexity in economics are presented, including models with heterogeneous, interacting agents, cybernetics, catastrophe theory, chaos theory, and modern complexity theory.
Abstract: Complex dynamics in economics arise from nonlinear systems that do not converge to a fixed point, a limit cycle, or explode or implode exponentially due to endogenous factors. They arise from cybernetics, catastrophe theory, chaos theory, or the varieties of modern complexity theory, including models with heterogeneous, interacting agents. This major three-volume collection presents the most important papers in the area of complexity in economics.
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TL;DR: In this paper, the authors investigate the dynamics in a simple present discounted value asset pricing model with heterogeneous beliefs, where agents choose from a finite set of predictors of future prices of a risky asset and revise their "beliefs" in each period in a boundedly rational way, according to a fitness measure such as past realized profits.
1,735 citations
TL;DR: In this paper, the authors describe a multi-agent model of financial markets which supports the idea that scaling arises from mutual interactions of participants, and they find that it generates such behaviour as a result of interactions between agents.
Abstract: Financial prices have been found to exhibit some universal characteristics1,2,3,4,5,6 that resemble the scaling laws characterizing physical systems in which large numbers of units interact. This raises the question of whether scaling in finance emerges in a similar way — from the interactions of a large ensemble of market participants. However, such an explanation is in contradiction to the prevalent ‘efficient market hypothesis’7 in economics, which assumes that the movements of financial prices are an immediate and unbiased reflection of incoming news about future earning prospects. Within this hypothesis, scaling in price changes would simply reflect similar scaling in the ‘input’ signals that influence them. Here we describe a multi-agent model of financial markets which supports the idea that scaling arises from mutual interactions of participants. Although the ‘news arrival process’ in our model lacks both power-law scaling and any temporal dependence in volatility, we find that it generates such behaviour as a result of interactions between agents.
1,504 citations
TL;DR: The robustness provided by the consideration of multiple possible futures has served several groups well; the authors present examples from business, government, and conservation planning that illustrate the value of scenario planning.
Abstract: Conservation decisions about how, when, and where to act are typically based on our expectations for the future. When the world is highly unpredictable and we are working from a limited range of expecta- tions, however, our expectations will frequently be proved wrong. Scenario planning offers a framework for developing more resilient conservation policies when faced with uncontrollable, irreducible uncertainty. A scenario in this context is an account of a plausible future. Scenario planning consists of using a few con- trasting scenarios to explore the uncertainty surrounding the future consequences of a decision. Ideally, sce- narios should be constructed by a diverse group of people for a single, stated purpose. Scenario planning can incorporate a variety of quantitative and qualitative information in the decision-making process. Often, con- sideration of this diverse information in a systemic way leads to better decisions. Furthermore, the participa- tion of a diverse group of people in a systemic process of collecting, discussing, and analyzing scenarios builds shared understanding. The robustness provided by the consideration of multiple possible futures has served several groups well; we present examples from business, government, and conservation planning that illustrate the value of scenario planning. For conservation, major benefits of using scenario planning are (1) increased understanding of key uncertainties, (2) incorporation of alternative perspectives into conservation planning, and (3) greater resilience of decisions to surprise.
1,265 citations
TL;DR: A survey of dynamic heterogeneous agent models (HAMs) in economics and finance can be found in this article, where the authors focus on simple models that are tractable by analytic methods in combination with computational tools.
Abstract: This chapter surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect, but perform reasonably well. Typically these models are highly nonlinear, e.g. due to evolutionary switching between strategies, and exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex, chaotic dynamics. Aggregation of simple interactions at the micro level may generate sophisticated structure at the macro level. Simple HAMs can explain important observed stylized facts in financial time series, such as excess volatility, high trading volume, temporary bubbles and trend following, sudden crashes and mean reversion, clustered volatility and fat tails in the returns distribution.
892 citations
TL;DR: In this paper, both chartist and fundamentalist strategies are considered with agents switching between both behavioural variants according to observed differences in pay-offs. But, the authors do not consider the effect of market makers on price changes.
Abstract: The finding of clustered volatility and ARCH effects is ubiquitous in financial data. This paper presents a possible explanation for this phenomenon within a multi-agent framework of speculative activity. In the model, both chartist and fundamentalist strategies are considered with agents switching between both behavioural variants according to observed differences in pay-offs. Price changes are brought about by a market maker reacting to imbalances between demand and supply. Most of the time, a stable and efficient market results. However, its usual tranquil performance is interspersed by sudden transient phases of destabilisation. An outbreak of volatility occurs if the fraction of agents using chartist techniques surpasses a certain threshold value, but such phases are quickly brought to an end by stabilising tendencies. Formally, this pattern can be understood as an example of a new type of dynamic behaviour known as "on-off intermittency" in physics literature. Statistical analysis of simulated time ...
740 citations