6 research outputs found
Error-driven Global Transition in a Competitive Population on a Network
We show, both analytically and numerically, that erroneous data transmission
generates a global transition within a competitive population playing the
Minority Game on a network. This transition, which resembles a phase
transition, is driven by a `temporal symmetry breaking' in the global outcome
series. The phase boundary, which is a function of the network connectivity
and the error probability , is described quantitatively by the
Crowd-Anticrowd theory.Comment: 4 pages, 3 figure
Competitive Advantage for Multiple-Memory Strategies in an Artificial Market
We consider a simple binary market model containing competitive agents.
The novel feature of our model is that it incorporates the tendency shown by
traders to look for patterns in past price movements over multiple time scales,
i.e. {\em multiple memory-lengths}. In the regime where these memory-lengths
are all small, the average winnings per agent exceed those obtained for either
(1) a pure population where all agents have equal memory-length, or (2) a mixed
population comprising sub-populations of equal-memory agents with each
sub-population having a different memory-length. Agents who consistently play
strategies of a given memory-length, are found to win more on average --
switching between strategies with different memory lengths incurs an effective
penalty, while switching between strategies of equal memory does not. Agents
employing short-memory strategies can outperform agents using long-memory
strategies, even in the regime where an equal-memory system would have favored
the use of long-memory strategies. Using the many-body `Crowd-Anticrowd'
theory, we obtain analytic expressions which are in good agreement with the
observed numerical results. In the context of financial markets, our results
suggest that multiple-memory agents have a better chance of identifying price
patterns of unknown length and hence will typically have higher winnings.Comment: Talk to be given at the SPIE conference on Econophysics and Finance,
in the International Symposium 'Fluctuations and Noise', 23-26 May 2005 in
Austin, Texa
Models of complex adaptive systems with underlying network structure
This thesis explores the effect of different types of underlying network structure on the dynamical behaviour of a competitive population - a situation encountered in many real-world complex systems. In the first part of the thesis, I focus on generic, but abstract, multi-agent systems. I start by presenting analytic and numerical results for a population of heterogeneous, decision-making agents competing for some limited global resource, in which connections arise unintentionally between agents as a by-product of their strategy choices. I show that accounting for the resulting groups of strongly-correlated agents - in particular, the crowds and so-called 'anticrowds' - yields an accurate analytic description of the systems dynamics. I then introduce a local communication network between the agents, enabling them to intentionally share information among themselves. Such an interaction network leads to highly non-trivial dynamics, forcing a trade-off between individual and global success. Introducing corruption into the information being exchanged between agents, gives rise to a novel phase transition. I then provide a quantitative analytic theory of these various numerical results by generalizing the Crowd-Anticrowd formalism to include such local interactions. In the second part of the thesis, I consider a real-world system which also features competitive populations and networks - a cancer tumour, which contains cancerous cells competing for space and nutrients in the presence of an underlying vasculature structure. To simplify the analysis and comparison to real clinical data, the model chosen is far simpler than that discussed in the first part of the thesis - however despite its simplicity, the model is shown to yield remarkably good agreement with empirical findings. In addition, the model shows how different treatment methods can lead to a wide variety of unexpected re-growth behaviours of the tumour
Models of complex adaptive systems with underlying network structure
This thesis explores the effect of different types of underlying network structure on the dynamical behaviour of a competitive population - a situation encountered in many real-world complex systems. In the first part of the thesis, I focus on generic, but abstract, multi-agent systems. I start by presenting analytic and numerical results for a population of heterogeneous, decision-making agents competing for some limited global resource, in which connections arise unintentionally between agents as a by-product of their strategy choices. I show that accounting for the resulting groups of strongly-correlated agents - in particular, the crowds and so-called 'anticrowds' - yields an accurate analytic description of the systems dynamics. I then introduce a local communication network between the agents, enabling them to intentionally share information among themselves. Such an interaction network leads to highly non-trivial dynamics, forcing a trade-off between individual and global success. Introducing corruption into the information being exchanged between agents, gives rise to a novel phase transition. I then provide a quantitative analytic theory of these various numerical results by generalizing the Crowd-Anticrowd formalism to include such local interactions. In the second part of the thesis, I consider a real-world system which also features competitive populations and networks - a cancer tumour, which contains cancerous cells competing for space and nutrients in the presence of an underlying vasculature structure. To simplify the analysis and comparison to real clinical data, the model chosen is far simpler than that discussed in the first part of the thesis - however despite its simplicity, the model is shown to yield remarkably good agreement with empirical findings. In addition, the model shows how different treatment methods can lead to a wide variety of unexpected re-growth behaviours of the tumour.</p
Models of complex adaptive systems with underlying network structure
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