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<div class="section" id="developing-new-models">
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<h1>Developing new models<a class="headerlink" href="#developing-new-models" title="Permalink to this headline">¶</a></h1>
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<p>This document describes how to develop a new analysis model.</p>
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<div class="section" id="what-is-a-model">
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<h2>What is a model?<a class="headerlink" href="#what-is-a-model" title="Permalink to this headline">¶</a></h2>
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<p>A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
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In practice, a model consists of at least two parts:</p>
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<ul class="simple">
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<li>Python module: the actual code that describes the behaviour.</li>
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<li>Setting up the variables in the Simulation Settings JSON file.</li>
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</ul>
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<p>This separation allows us to run the simulation with different agents.</p>
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</div>
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<div class="section" id="models-code">
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<h2>Models Code<a class="headerlink" href="#models-code" title="Permalink to this headline">¶</a></h2>
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<p>All the models are imported to the main file. The initialization look like this:</p>
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<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">settings</span>
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<span class="n">networkStatus</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># Dict that will contain the status of every agent in the network</span>
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<span class="n">sentimentCorrelationNodeArray</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">):</span>
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<span class="n">sentimentCorrelationNodeArray</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'id'</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="c1"># Initialize agent states. Let's assume everyone is normal.</span>
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<span class="n">init_states</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'id'</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="p">}</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">)]</span>
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<span class="c1"># add keys as as necessary, but "id" must always refer to that state category</span>
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</pre></div>
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</div>
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<p>A new model have to inherit the BaseBehaviour class which is in the same module.
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There are two basics methods:</p>
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<ul class="simple">
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<li>__init__</li>
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<li>step: used to define the behaviour over time.</li>
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</ul>
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</div>
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<div class="section" id="variable-initialization">
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<h2>Variable Initialization<a class="headerlink" href="#variable-initialization" title="Permalink to this headline">¶</a></h2>
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<p>The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
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passed as a parameter to the simulation.</p>
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<div class="code json highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
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<span class="s2">"agent"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SISaModel"</span><span class="p">,</span><span class="s2">"ControlModelM2"</span><span class="p">],</span>
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<span class="s2">"neutral_discontent_spon_prob"</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
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<span class="s2">"neutral_discontent_infected_prob"</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
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<span class="s2">"neutral_content_spon_prob"</span><span class="p">:</span> <span class="mf">0.18</span><span class="p">,</span>
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<span class="s2">"neutral_content_infected_prob"</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
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<span class="s2">"discontent_neutral"</span><span class="p">:</span> <span class="mf">0.13</span><span class="p">,</span>
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<span class="s2">"discontent_content"</span><span class="p">:</span> <span class="mf">0.07</span><span class="p">,</span>
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<span class="s2">"variance_d_c"</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
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<span class="s2">"content_discontent"</span><span class="p">:</span> <span class="mf">0.009</span><span class="p">,</span>
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<span class="s2">"variance_c_d"</span><span class="p">:</span> <span class="mf">0.003</span><span class="p">,</span>
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<span class="s2">"content_neutral"</span><span class="p">:</span> <span class="mf">0.088</span><span class="p">,</span>
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<span class="s2">"standard_variance"</span><span class="p">:</span> <span class="mf">0.055</span><span class="p">,</span>
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<span class="s2">"prob_neutral_making_denier"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
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<span class="s2">"prob_infect"</span><span class="p">:</span> <span class="mf">0.075</span><span class="p">,</span>
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<span class="s2">"prob_cured_healing_infected"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
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<span class="s2">"prob_cured_vaccinate_neutral"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
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<span class="s2">"prob_vaccinated_healing_infected"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
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<span class="s2">"prob_vaccinated_vaccinate_neutral"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
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<span class="s2">"prob_generate_anti_rumor"</span><span class="p">:</span> <span class="mf">0.035</span>
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<span class="p">}</span>
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</pre></div>
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</div>
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<p>In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
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The simulation returns one result for each model. For the usage, see <a class="reference internal" href="usage.html"><span class="doc">Usage</span></a>.</p>
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</div>
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<div class="section" id="example-model">
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<h2>Example Model<a class="headerlink" href="#example-model" title="Permalink to this headline">¶</a></h2>
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<p>In this section, we will implement a Sentiment Correlation Model.</p>
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<p>The class would look like this:</p>
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<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">..BaseBehaviour</span> <span class="k">import</span> <span class="o">*</span>
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<span class="kn">from</span> <span class="nn">..</span> <span class="k">import</span> <span class="n">sentimentCorrelationNodeArray</span>
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<span class="k">class</span> <span class="nc">SentimentCorrelationModel</span><span class="p">(</span><span class="n">BaseBehaviour</span><span class="p">):</span>
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<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">environment</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">agent_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">state</span><span class="o">=</span><span class="p">()):</span>
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<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="n">environment</span><span class="o">=</span><span class="n">environment</span><span class="p">,</span> <span class="n">agent_id</span><span class="o">=</span><span class="n">agent_id</span><span class="p">,</span> <span class="n">state</span><span class="o">=</span><span class="n">state</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">outside_effects_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'outside_effects_prob'</span><span class="p">]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">anger_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'anger_prob'</span><span class="p">]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">joy_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'joy_prob'</span><span class="p">]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">sadness_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'sadness_prob'</span><span class="p">]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">disgust_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'disgust_prob'</span><span class="p">]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">time_awareness</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span> <span class="c1"># In this model we have 4 sentiments</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">time_awareness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust</span>
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<span class="n">sentimentCorrelationNodeArray</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="o">.</span><span class="n">now</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">now</span><span class="p">):</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">behaviour</span><span class="p">()</span> <span class="c1"># Method which define the behaviour</span>
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<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">now</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.</p>
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<h3><a href="index.html">Table Of Contents</a></h3>
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<ul>
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<li><a class="reference internal" href="#">Developing new models</a><ul>
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<li><a class="reference internal" href="#what-is-a-model">What is a model?</a></li>
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<li><a class="reference internal" href="#models-code">Models Code</a></li>
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<li><a class="reference internal" href="#variable-initialization">Variable Initialization</a></li>
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<li><a class="reference internal" href="#example-model">Example Model</a></li>
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<li><a href="index.html">Documentation overview</a><ul>
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<li>Previous: <a href="usage.html" title="previous chapter">Usage</a></li>
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