33,931 research outputs found
Programmable models of growth and mutation of cancer-cell populations
In this paper we propose a systematic approach to construct mathematical
models describing populations of cancer-cells at different stages of disease
development. The methodology we propose is based on stochastic Concurrent
Constraint Programming, a flexible stochastic modelling language. The
methodology is tested on (and partially motivated by) the study of prostate
cancer. In particular, we prove how our method is suitable to systematically
reconstruct different mathematical models of prostate cancer growth - together
with interactions with different kinds of hormone therapy - at different levels
of refinement.Comment: In Proceedings CompMod 2011, arXiv:1109.104
Consentaneous agent-based and stochastic model of the financial markets
We are looking for the agent-based treatment of the financial markets
considering necessity to build bridges between microscopic, agent based, and
macroscopic, phenomenological modeling. The acknowledgment that agent-based
modeling framework, which may provide qualitative and quantitative
understanding of the financial markets, is very ambiguous emphasizes the
exceptional value of well defined analytically tractable agent systems. Herding
as one of the behavior peculiarities considered in the behavioral finance is
the main property of the agent interactions we deal with in this contribution.
Looking for the consentaneous agent-based and macroscopic approach we combine
two origins of the noise: exogenous one, related to the information flow, and
endogenous one, arising form the complex stochastic dynamics of agents. As a
result we propose a three state agent-based herding model of the financial
markets. From this agent-based model we derive a set of stochastic differential
equations, which describes underlying macroscopic dynamics of agent population
and log price in the financial markets. The obtained solution is then subjected
to the exogenous noise, which shapes instantaneous return fluctuations. We test
both Gaussian and q-Gaussian noise as a source of the short term fluctuations.
The resulting model of the return in the financial markets with the same set of
parameters reproduces empirical probability and spectral densities of absolute
return observed in New York, Warsaw and NASDAQ OMX Vilnius Stock Exchanges. Our
result confirms the prevalent idea in behavioral finance that herding
interactions may be dominant over agent rationality and contribute towards
bubble formation.Comment: 17 pages, 6 figures, Gontis V, Kononovicius A (2014) Consentaneous
Agent-Based and Stochastic Model of the Financial Markets. PLoS ONE 9(7):
e102201. doi: 10.1371/journal.pone.010220
Investigating biocomplexity through the agent-based paradigm.
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex
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