Upload app.py

app.py

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+import pandas as pd
+import numpy as np
+from random import normalvariate, random
+import plotly.express as px
+
+from radcad import Model, Simulation, Experiment
+import streamlit as st
+
+
+# Additional dependencies
+
+# For analytics
+import numpy as np
+# For visualization
+import plotly.express as px
+pd.options.plotting.backend = "plotly"
+
+st.header('DeSci Value Flow Model')
+
+def p_researcher1(params, substep, state_history, previous_state):
+    losses = 0
+    to_market = 0
+    to_researcher = 0
+    to_treasury = 0
+    to_other_researcher = 0
+    salary = 0
+    funding = 0
+    if (previous_state['timestep'] < params['timestep_switch']) and (previous_state['funding_pool'] > params['funding_round']):
+        funding = params['funding_round']
+        to_treasury -= funding
+        research_value = funding * (1-params['epsilon'])
+        losses += funding - research_value
+
+        salary = research_value * params['beta']
+        to_market = research_value
+        if (random() < params['probability_buying']) and (previous_state['researcher1_value'] > params['cost_buying']):
+            salary = salary - params['cost_buying']
+            tx_fee = params['cost_buying'] * params['tx_fee']
+            salary -= tx_fee
+            to_treasury += tx_fee
+            to_other_researcher += params['cost_buying']
+        to_researcher += salary + research_value
+    elif (previous_state['timestep'] > params['timestep_switch']) and (previous_state['researcher1_value'] > params['cost_buying']):
+        tx_fee = params['cost_buying'] * params['tx_fee']
+        to_researcher -= params['cost_buying'] - tx_fee
+        to_other_researcher += params['cost_buying']
+        to_treasury += tx_fee
+    return {'update_researcher1_funding': funding,
+                'update_researcher1_salary': salary,
+                'update_researcher1_value': to_researcher,
+                'update_funding_pool': to_treasury,
+                'update_market': to_market,
+                'update_researcher2_value': to_other_researcher,
+                'update_losses': losses}
+
+def p_researcher2(params, substep, state_history, previous_state):
+    losses = 0
+    to_market = 0
+    to_researcher = 0
+    to_treasury = 0
+    to_other_researcher = 0
+    salary = 0
+    funding = 0
+    if (previous_state['timestep'] > params['timestep_switch']) and (previous_state['funding_pool'] > params['funding_round']):
+        funding = params['funding_round']
+        to_treasury -= funding
+        research_value = funding * (1-params['epsilon'])
+        losses += funding - research_value
+
+        salary = research_value * params['beta']
+        to_market = research_value
+        if (random() < params['probability_buying']) and (previous_state['researcher2_value'] > params['cost_buying']):
+            salary = salary - params['cost_buying']
+            tx_fee = params['cost_buying'] * params['tx_fee']
+            salary -= tx_fee
+            to_treasury += tx_fee
+            to_other_researcher += params['cost_buying']
+        to_researcher += salary + research_value
+    elif (previous_state['timestep'] < params['timestep_switch']) and (previous_state['researcher2_value'] > params['cost_buying']):
+        tx_fee = params['cost_buying'] * params['tx_fee']
+        to_researcher -= params['cost_buying'] - tx_fee
+        to_other_researcher += params['cost_buying']
+        to_treasury += tx_fee
+    return {'update_researcher2_funding': funding,
+                'update_researcher2_salary': salary,
+                'update_researcher2_value': to_researcher,
+                'update_funding_pool': to_treasury,
+                'update_market': to_market,
+                'update_researcher1_value': to_other_researcher,
+                'update_losses': losses}
+
+def s_timestep(params, substep, state_history, previous_state, policy_input):
+    updated_timestep = previous_state['timestep'] + 1
+    return 'timestep', updated_timestep
+
+def s_funding_pool(params, substep, state_history, previous_state, policy_input):
+    funding_pool = previous_state['funding_pool']
+    updated_funding_pool = funding_pool + policy_input['update_funding_pool']
+    return 'funding_pool', updated_funding_pool
+
+def s_researcher1_value(params, substep, state_history, previous_state, policy_input):
+    r_value = previous_state['researcher1_value']
+    updated_researcher1_value = r_value + policy_input['update_researcher1_value']
+    return 'researcher1_value', updated_researcher1_value
+
+def s_researcher1_funding(params, substep, state_history, previous_state, policy_input):
+    r_funding = previous_state['researcher1_funding']
+    updated_researcher1_funding = r_funding + policy_input['update_researcher1_funding']
+    return 'researcher1_funding', updated_researcher1_funding
+
+def s_researcher1_salary(params, substep, state_history, previous_state, policy_input):
+    r_salary = previous_state['researcher1_salary']
+    updated_researcher1_salary = r_salary + policy_input['update_researcher1_salary']
+    return 'researcher1_salary', updated_researcher1_salary
+
+def s_researcher2_value(params, substep, state_history, previous_state, policy_input):
+    r_value = previous_state['researcher2_value']
+    updated_researcher2_value = r_value + policy_input['update_researcher2_value']
+    return 'researcher2_value', updated_researcher2_value
+
+def s_researcher2_funding(params, substep, state_history, previous_state, policy_input):
+    r_funding = previous_state['researcher2_funding']
+    updated_researcher2_funding = r_funding + policy_input['update_researcher2_funding']
+    return 'researcher2_funding', updated_researcher2_funding
+
+def s_researcher2_salary(params, substep, state_history, previous_state, policy_input):
+    r_salary = previous_state['researcher2_salary']
+    updated_researcher2_salary = r_salary + policy_input['update_researcher2_salary']
+    return 'researcher2_salary', updated_researcher2_salary
+
+def s_knowledge_market(params, substep, state_history, previous_state, policy_input):
+    value = previous_state['knowledge_market_value']
+    updated_market_value = value + policy_input['update_market']
+    return 'knowledge_market_value', updated_market_value
+
+def s_losses(params, substep, state_history, previous_state, policy_input):
+    losses = previous_state['losses']
+    updated_losses = losses + policy_input['update_losses']
+    return 'losses', updated_losses
+
+st.subheader('Initial Value Allocation')
+funding_pool = st.slider('Initial Funding Pool', min_value=1000, max_value=10000, value=1000, step=10)
+researcher1_value = st.slider('Researcher1 Tokens', min_value=0, max_value=1000, value=0, step=1)
+researcher2_value = st.slider('Researcher2 Tokens', min_value=0, max_value=1000, value=0, step=1)
+st.subheader('Simulation Parameters')
+tx_fee = st.slider('Transaction fee collected by DAO treasury during each transaction in the knowledge market', min_value=0., max_value=1., value=0.1, step=0.0001)
+st.write('Set the funding disbursed each round from the funding pool')
+funding_round = st.slider('Funding Round', min_value=100, max_value=1000, value=100, step=1)
+st.write('Set the relative value leakages in the model.')
+epsilon = st.slider('Work Inefficiency Weight', min_value=0., max_value=1., value=0.1, step=0.0001)
+st.write('Set the portion of grant funding to be used as researcher salary.')
+beta = st.slider('Salary Weight', min_value=0., max_value=1., value=0.4, step=0.0001)
+st.write('Set the cost of getting access to papers in the knowledge market.')
+cost_buying = st.slider('Cost of Buying', min_value=10., max_value=100., value=10., step=0.1)
+st.write('Set the probability a researcher will buy access to a paper at each timestep.')
+probability_buying = st.slider('Researcher Probability of Buying', min_value=0., max_value=1., value=0.1, step=0.0001)
+st.write('Set the number of timesteps in the simulation.')
+timesteps = st.slider('Timesteps', min_value=10, max_value=1000, value=100, step=1)
+
+initial_state = {
+    'funding_pool': funding_pool,
+    'researcher1_value': researcher1_value,
+    'researcher1_funding': 0,
+    'researcher1_salary': 0,
+    'researcher2_value': researcher2_value,
+    'researcher2_funding': 0,
+    'researcher2_salary': 0,
+    'knowledge_market_value': 0,
+    'timestep': 0,
+    'losses': 0
+}
+ts = int(timesteps/2)
+
+system_params = {
+    'funding_pool': [funding_pool],
+    'funding_round': [funding_round],
+    'beta': [beta],
+    'epsilon': [epsilon],
+    'cost_buying': [cost_buying],
+    'probability_buying': [probability_buying],
+    'timestep_switch': [ts],
+    'tx_fee': [tx_fee]
+}
+
+def configure_and_run_experiment(initial_state,
+                      partial_state_update_blocks,
+                      timesteps):
+    model = Model(
+    # Model initial state
+    initial_state=initial_state,
+    # Model Partial State Update Blocks
+    state_update_blocks=partial_state_update_blocks,
+    # System Parameters
+    params=system_params
+    )
+    simulation = Simulation(
+    model=model,
+    timesteps=timesteps,  # Number of timesteps
+    runs=1  # Number of Monte Carlo Runs
+    )   
+
+    result = simulation.run()
+    return result
+
+partial_state_update_blocks = [
+    {
+        'policies': {
+            'p_researcher1': p_researcher1,
+            'p_researcher2': p_researcher2
+        },
+        'variables': {
+            'timestep': s_timestep,
+            'funding_pool': s_funding_pool,
+            'researcher1_value': s_researcher1_value,
+            'researcher1_funding': s_researcher1_funding,
+            'researcher1_salary': s_researcher1_salary,
+            'researcher2_value': s_researcher2_value,
+            'researcher2_funding': s_researcher2_funding,
+            'researcher2_salary': s_researcher2_salary,
+            'knowledge_market_value': s_knowledge_market,
+            'losses': s_losses
+        }
+    }
+]
+
+if st.button('Run Simulation'):
+    raw_result = configure_and_run_experiment(initial_state, partial_state_update_blocks, timesteps)
+    df = pd.DataFrame(raw_result)
+    fig1 = df.plot(kind='line', x='timestep', y=['funding_pool', 'researcher1_value', 'researcher2_value'], width=1000)
+    fig2 = df.plot(kind='line', x='timestep', y=['funding_pool','knowledge_market_value'], width=1000)
+    fig3 = df.plot(kind='line', x='timestep', y=['funding_pool', 'losses'], width=1000)
+    fig4 = df.plot(kind='line', x='timestep', y=['researcher1_value', 'researcher2_value', 'losses'], width=1000)
+    st.subheader('Results')
+    st.plotly_chart(fig1)
+    st.plotly_chart(fig2)
+    st.plotly_chart(fig3)
+    st.plotly_chart(fig4)