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 <h1 xmlns:l="http://docbook.sourceforge.net/xmlns/l10n/1.0">Agent-Based Modeling</h1>
 <div class="section" title="Agent-Based Modeling">
 <div class="titlepage">
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 <h2 class="title" style="clear: both">
 <a name="Agent-Based_Modeling"></a>Agent-Based Modeling</h2>
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 <p>The primary focus of the Agent Modeling Platform tools is "Agent-Based Modeling" (ABM). ABM is an innovative technique used to explore complex phenomenon in many domains, including economics, social sciences, biomedicine, ecology and business operations. ABMs share characteristics with object models, but are:</p>
 <div class="variablelist">
 <dl>
 <dt>
 <span class="term">Spatial</span>
 </dt>
 <dd>
 <p>Models have explicit environment(s) in which agents interact. (An environment need not be a physical landscape; other examples of spatial relationships include social networks or positions within a logic system.)</p>
 </dd>
 <dt>
 <span class="term">Temporal</span>
 </dt>
 <dd>
 <p>Models change over discrete units of time.</p>
 </dd>
 <dt>
 <span class="term">Autonomous</span>
 </dt>
 <dd>
 <p>Agent behaviors are activated independently from other object requests.</p>
 </dd>
 <dt>
 <span class="term">Heterogeneous</span>
 </dt>
 <dd>
 <p>Agents may share behavior definitions but have apparent and distinct states and behaviors.</p>
 </dd>
 <dt>
 <span class="term">Collective</span>
 </dt>
 <dd>
 <p>Models contain large communities of agents which exhibit collaborative and competitive behaviors.</p>
 </dd>
 <dt>
 <span class="term">Emergent</span>
 </dt>
 <dd>
 <p>Agents have collective macro-behaviors that are non-obvious from agent micro-specifications.</p>
 </dd>
 </dl>
 </div>
 <p>Existing scientific models are very good at representing relatively simple systems, but generally speaking aren't very good at representing complex systems. The world is full of complex systems, and our misunderstanding of these systems has prevented us from addressing many of the key challenges facing the world, including the global financial crisis and climate change -- in fact once could argue that our misunderstanding of these systems has strongly contributed to these crises.</p>
 <p>Agent-Based Models (ABMs) seek to represent important real-world dynamics by designing communities of software agents that mimic real entities. Rather than make simplifying assumptions about such systems and then representing them in equation form or as off the shelf algorithmic constructs, the ABM researcher aims to identify key agent state, interaction spaces, and behaviors. Agents are then "let loose" on our computers and we explore what happens next. The computational horsepower exists today to simulate large numbers (e.g. &gt;&gt;10) of interacting, adaptive and autonomous agents but often desktop computers are all we need to explore significant domains. ABMs have been designed to represent all kinds of important natural systems, at scales reaching from cellular mechanics to international trade and are being used to solve truly hard problems in government, business, and academia. ABMs are not a solution to every problem, but they can help us to appreciate and gain unique insight into many systems, and often they can help us to come up with better practical decisions than we might using classic approaches.</p>
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	<title>Agent-Based Modeling</title>
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	<meta content="DocBook XSL Stylesheets V1.75.1" name="generator">
	<link rel="home" href="index.html" title="Agent Modeling Guide">
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	<h1 xmlns:l="http://docbook.sourceforge.net/xmlns/l10n/1.0">Agent-Based Modeling</h1>
	<div class="section" title="Agent-Based Modeling">
	<div class="titlepage">
	<div>
	<div>
	<h2 class="title" style="clear: both">
	<a name="Agent-Based_Modeling"></a>Agent-Based Modeling</h2>
	</div>
	</div>
	</div>
	<p>The primary focus of the Agent Modeling Platform tools is "Agent-Based Modeling" (ABM). ABM is an innovative technique used to explore complex phenomenon in many domains, including economics, social sciences, biomedicine, ecology and business operations. ABMs share characteristics with object models, but are:</p>
	<div class="variablelist">
	<dl>
	<dt>
	<span class="term">Spatial</span>
	</dt>
	<dd>
	<p>Models have explicit environment(s) in which agents interact. (An environment need not be a physical landscape; other examples of spatial relationships include social networks or positions within a logic system.)</p>
	</dd>
	<dt>
	<span class="term">Temporal</span>
	</dt>
	<dd>
	<p>Models change over discrete units of time.</p>
	</dd>
	<dt>
	<span class="term">Autonomous</span>
	</dt>
	<dd>
	<p>Agent behaviors are activated independently from other object requests.</p>
	</dd>
	<dt>
	<span class="term">Heterogeneous</span>
	</dt>
	<dd>
	<p>Agents may share behavior definitions but have apparent and distinct states and behaviors.</p>
	</dd>
	<dt>
	<span class="term">Collective</span>
	</dt>
	<dd>
	<p>Models contain large communities of agents which exhibit collaborative and competitive behaviors.</p>
	</dd>
	<dt>
	<span class="term">Emergent</span>
	</dt>
	<dd>
	<p>Agents have collective macro-behaviors that are non-obvious from agent micro-specifications.</p>
	</dd>
	</dl>
	</div>
	<p>Existing scientific models are very good at representing relatively simple systems, but generally speaking aren't very good at representing complex systems. The world is full of complex systems, and our misunderstanding of these systems has prevented us from addressing many of the key challenges facing the world, including the global financial crisis and climate change -- in fact once could argue that our misunderstanding of these systems has strongly contributed to these crises.</p>
	<p>Agent-Based Models (ABMs) seek to represent important real-world dynamics by designing communities of software agents that mimic real entities. Rather than make simplifying assumptions about such systems and then representing them in equation form or as off the shelf algorithmic constructs, the ABM researcher aims to identify key agent state, interaction spaces, and behaviors. Agents are then "let loose" on our computers and we explore what happens next. The computational horsepower exists today to simulate large numbers (e.g. >>10) of interacting, adaptive and autonomous agents but often desktop computers are all we need to explore significant domains. ABMs have been designed to represent all kinds of important natural systems, at scales reaching from cellular mechanics to international trade and are being used to solve truly hard problems in government, business, and academia. ABMs are not a solution to every problem, but they can help us to appreciate and gain unique insight into many systems, and often they can help us to come up with better practical decisions than we might using classic approaches.</p>
	</div>
	</body>
	</html>