62,776 research outputs found
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...
There are indications that the current generation of simulation models in practical,
operational uses has reached the limits of its usefulness under existing specifications.
The relative stasis in operational urban modeling contrasts with simulation efforts in
other disciplines, where techniques, theories, and ideas drawn from computation and
complexity studies are revitalizing the ways in which we conceptualize, understand,
and model real-world phenomena. Many of these concepts and methodologies are
applicable to operational urban systems simulation. Indeed, in many cases, ideas from
computation and complexity studiesâoften clustered under the collective term of
geocomputation, as they apply to geographyâare ideally suited to the simulation of
urban dynamics. However, there exist several obstructions to their successful use in
operational urban geographic simulation, particularly as regards the capacity of these
methodologies to handle top-down dynamics in urban systems.
This paper presents a framework for developing a hybrid model for urban geographic
simulation and discusses some of the imposing barriers against innovation in this
field. The framework infuses approaches derived from geocomputation and
complexity with standard techniques that have been tried and tested in operational
land-use and transport simulation. Macro-scale dynamics that operate from the topdown
are handled by traditional land-use and transport models, while micro-scale
dynamics that work from the bottom-up are delegated to agent-based models and
cellular automata. The two methodologies are fused in a modular fashion using a
system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of
residential location has been developed with a view to hybridization. The model
mixes cellular automata and multi-agent approaches and is formulated so as to
interface with meso-models at a higher scale
Nature as a Network of Morphological Infocomputational Processes for Cognitive Agents
This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
From metagenomics to the metagenome: Conceptual change and the rhetoric of translational genomic research
As the international genomic research community moves from the tool-making efforts of the Human Genome Project into biomedical applications of those tools, new metaphors are being suggested as useful to understanding how our genes work â and for understanding who we are as biological organisms. In this essay we focus on the Human Microbiome Project as one such translational initiative. The HMP is a new âmetagenomicâ research effort to sequence the genomes of human microbiological flora, in order to pursue the interesting hypothesis that our âmicrobiomeâ plays a vital and interactive role with our human genome in normal human physiology. Rather than describing the human genome as the âblueprintâ for human nature, the promoters of the HMP stress the ways in which our primate lineage DNA is interdependent with the genomes of our microbiological flora. They argue that the human body should be understood as an ecosystem with multiple ecological niches and habitats in which a variety of cellular species collaborate and compete, and that human beings should be understood as âsuperorganismsâ that incorporate multiple symbiotic cell species into a single individual with very blurry boundaries. These metaphors carry interesting philosophical messages, but their inspiration is not entirely ideological. Instead, part of their cachet within genome science stems from the ways in which they are rooted in genomic research techniques, in what philosophers of science have called a âtools-to-theoryâ heuristic. Their emergence within genome science illustrates the complexity of conceptual change in translational research, by showing how it reflects both aspirational and methodological influences
The agricultural policy simulator (AgriPoliS): an agent-based model to study structural change in agriculture (Version 1.0)
A central criticism common to agricultural economic modelling approaches for policy analysis is that they do not adequately take account of a number of characteristic factors of the agricultural sector. This concerns aspects like the immobility of land, heterogeneity of farms, interactions between farms, space, dynamic adjustment processes as well as dynamics of structural change. In brief, modelling the complexity of the system has not been at the centre of interest. In terms of modelling complex economic systems, an agent-based modelling approach is a suitable approach to quantitatively model and understand such systems in a more natural way. In the same way, this applies to the modelling of agricultural structures. In particular, agent-based models of agricultural structures allow for carrying out computer experiments to support a better understanding of the complexity of agricultural systems, structural change, and endogenous adjustment reactions in response to a policy change. This paper presents the agent-based model AgriPoliS (Agricultural Policy Simulator) which simultaneously considers a large number of individually acting farms, product markets, investment activity, as well as the land market, and a simple spatial representation. The ultimate objective of AgriPoliS is to study the interrelationship of rents, technical change, product prices, investments, production and policies, structural effects resulting from these, the analysis of the winners and losers of agricultural policy as well as the costs and efficiency of various policy measures. -- G E R M A N V E R S I O N: Ein oft genannter Kritikpunkt an vielen agrarökonomischen Politikanalysemodellen ist, dass diese nur ungenĂŒgend Bezug nehmen auf Aspekte wie die ImmobilitĂ€t von Boden, HeterogenitĂ€t der Akteure, Interaktionen zwischen Betrieben, rĂ€umliche BezĂŒge, dynamische Anpassungsprozesse und Strukturwandel. Kurz, die Modellierung komplexer WirkungszusammenhĂ€nge steht weniger oder nicht im Zentrum des Interesses. Agentenbasierte Modelle stellen einen Weg dar, das VerstĂ€ndnis komplexer ökonomischer ZusammenhĂ€nge zu verbessern bzw. zu quantifizieren. Insbesondere erlauben sie die DurchfĂŒhrung von einer Vielzahl von Computerexperimenten, mit denen Fragestellungen wie der Zusammenhang zwischen PolitikmaĂnahmen und Strukturwandel untersucht werden können. Basierend darauf, stellt dieser Beitrag das agentenbasierte Modell AgriPoliS (Agricultural Policy Simulator) vor. AgriPoliS ist ein rĂ€umlich-dynamisches Modell einer Agrarstruktur, in dem eine Vielzahl individuell abgebildeter landwirtschaftlicher Unternehmen in einer vereinfacht dargestellten Agrarregion agiert und beispielsweise um begrenzt verfĂŒgbare landwirtschaftliche FlĂ€chen konkurriert.Agent-based systems,Multi-agent systems,Policy analysis,Structural change,Simulation,Agentenbasierte Systeme,Politikanalyse,Multi-Agentensysteme,Strukturwandel,Simulation
Focal Spot, Spring 2009
https://digitalcommons.wustl.edu/focal_spot_archives/1111/thumbnail.jp
- âŠ