diff --git a/conviction_system_logic_sim.py b/conviction_system_logic_sim.py
new file mode 100644
index 0000000..502e232
--- /dev/null
+++ b/conviction_system_logic_sim.py
@@ -0,0 +1,547 @@
+import numpy as np
+
+from cadCAD.configuration.utils import config_sim
+from conviction_helpers import *
+#import networkx as nx
+from scipy.stats import expon, gamma
+
+
+#functions for partial state update block 1
+
+#Driving processes: arrival of participants, proposals and funds
+##-----------------------------------------
+def gen_new_participant(network, new_participant_holdings):
+    
+    i = len([node for node in network.nodes])
+    
+    network.add_node(i)
+    network.nodes[i]['type']="participant"
+    
+    s_rv = np.random.rand() 
+    network.nodes[i]['sentiment'] = s_rv
+    network.nodes[i]['holdings']=new_participant_holdings
+    
+    for j in get_nodes_by_type(network, 'proposal'):
+        network.add_edge(i, j)
+        
+        rv = np.random.rand()
+        a_rv = 1-4*(1-rv)*rv #polarized distribution
+        network.edges[(i, j)]['affinity'] = a_rv
+        network.edges[(i,j)]['tokens'] = a_rv*network.nodes[i]['holdings']
+        network.edges[(i, j)]['conviction'] = 0
+    
+    return network
+    
+
+scale_factor = 1000
+
+def gen_new_proposal(network, funds, supply, trigger_func):
+    j = len([node for node in network.nodes])
+    network.add_node(j)
+    network.nodes[j]['type']="proposal"
+    
+    network.nodes[j]['conviction']=0
+    network.nodes[j]['status']='candidate'
+    network.nodes[j]['age']=0
+    
+    rescale = scale_factor*funds
+    r_rv = gamma.rvs(3,loc=0.001, scale=rescale)
+    network.node[j]['funds_requested'] = r_rv
+    
+    network.nodes[j]['trigger']= trigger_func(r_rv, funds, supply)
+    
+    participants = get_nodes_by_type(network, 'participant')
+    proposing_participant = np.random.choice(participants)
+    
+    for i in participants:
+        network.add_edge(i, j)
+        if i==proposing_participant:
+            network.edges[(i, j)]['affinity']=1
+        else:
+            rv = np.random.rand()
+            a_rv = 1-4*(1-rv)*rv #polarized distribution
+            network.edges[(i, j)]['affinity'] = a_rv
+            
+        network.edges[(i, j)]['conviction'] = 0
+        network.edges[(i,j)]['tokens'] = 0
+    return network
+        
+        
+
+def driving_process(params, step, sL, s):
+    
+    #placeholder plumbing for random processes
+    arrival_rate = 10/s['sentiment']
+    rv1 = np.random.rand()
+    new_participant = bool(rv1<1/arrival_rate)
+    if new_participant:
+        h_rv = expon.rvs(loc=0.0, scale=1000)
+        new_participant_holdings = h_rv
+    else:
+        new_participant_holdings = 0
+    
+    network = s['network']
+    affinities = [network.edges[e]['affinity'] for e in network.edges ]
+    median_affinity = np.median(affinities)
+    
+    proposals = get_nodes_by_type(network, 'proposal')
+    fund_requests = [network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate' ]
+    
+    funds = s['funds']
+    total_funds_requested = np.sum(fund_requests)
+    
+    proposal_rate = 10/median_affinity * total_funds_requested/funds
+    rv2 = np.random.rand()
+    new_proposal = bool(rv2<1/proposal_rate)
+    
+    sentiment = s['sentiment']
+    funds = s['funds']
+    scale_factor = 1+4000*sentiment**2
+    
+    #this shouldn't happen but expon is throwing domain errors
+    if scale_factor > 1: 
+        funds_arrival = expon.rvs(loc = 0, scale = scale_factor )
+    else:
+        funds_arrival = 0
+    
+    return({'new_participant':new_participant,
+            'new_participant_holdings':new_participant_holdings,
+            'new_proposal':new_proposal, 
+            'funds_arrival':funds_arrival})
+
+    
+#Mechanisms for updating the state based on driving processes
+##---
+def update_network(params, step, sL, s, _input):
+
+    network = s['network']
+    funds = s['funds']
+    supply = s['supply']
+    trigger_func = params['trigger_func']
+
+    new_participant = _input['new_participant'] #T/F
+    new_proposal = _input['new_proposal'] #T/F
+
+    if new_participant:
+        new_participant_holdings = _input['new_participant_holdings']
+        network = gen_new_participant(network, new_participant_holdings)
+    
+    if new_proposal:
+        network= gen_new_proposal(network,funds,supply,trigger_func )
+    
+    #update age of the existing proposals
+    proposals = get_nodes_by_type(network, 'proposal')
+    
+    for j in proposals:
+        network.nodes[j]['age'] =  network.nodes[j]['age']+1
+        if network.nodes[j]['status'] == 'candidate':
+            requested = network.nodes[j]['funds_requested']
+            network.nodes[j]['trigger'] = trigger_func(requested, funds, supply)
+        else:
+            network.nodes[j]['trigger'] = np.nan
+            
+    key = 'network'
+    value = network
+    
+    return (key, value)
+
+def increment_funds(params, step, sL, s, _input):
+    
+    funds = s['funds']
+    funds_arrival = _input['funds_arrival']
+
+    #increment funds
+    funds = funds + funds_arrival
+    
+    key = 'funds'
+    value = funds
+    
+    return (key, value)
+
+def increment_supply(params, step, sL, s, _input):
+    
+    supply = s['supply']
+    supply_arrival = _input['new_participant_holdings']
+
+    #increment funds
+    supply = supply + supply_arrival
+    
+    key = 'supply'
+    value = supply
+    
+    return (key, value)
+
+#functions for partial state update block 2
+
+#Driving processes: completion of previously funded proposals
+##-----------------------------------------
+
+def check_progress(params, step, sL, s):
+    
+    network = s['network']
+    proposals = get_nodes_by_type(network, 'proposal')
+    
+    completed = []
+    for j in proposals:
+        if network.nodes[j]['status'] == 'active':
+            grant_size = network.nodes[j]['funds_requested']
+            base_completion_rate=params['base_completion_rate']
+            likelihood = 1.0/(base_completion_rate+np.log(grant_size))
+            if np.random.rand() < likelihood:
+                completed.append(j)
+    
+    return({'completed':completed})
+
+
+#Mechanisms for updating the state based on check progress
+##---
+def complete_proposal(params, step, sL, s, _input):
+    
+    network = s['network']
+    participants = get_nodes_by_type(network, 'participant')
+    
+    completed = _input['completed']
+    for j in completed:
+        network.nodes[j]['status']='completed'
+        for i in participants:
+            force = network.edges[(i,j)]['affinity']
+            sentiment = network.node[i]['sentiment']
+            network.node[i]['sentiment'] = get_sentimental(sentiment, force, decay=0)
+    
+    key = 'network'
+    value = network
+    
+    return (key, value)
+
+def update_sentiment_on_completion(params, step, sL, s, _input):
+    
+    network = s['network']
+    proposals = get_nodes_by_type(network, 'proposal')
+    completed = _input['completed']
+    
+    grants_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='active'])
+    
+    grants_completed = np.sum([network.nodes[j]['funds_requested'] for j in completed])
+    
+    sentiment = s['sentiment']
+    
+    force = grants_completed/grants_outstanding
+    mu = params['sentiment_decay']
+    if (force >=0) and (force <=1):
+        sentiment = get_sentimental(sentiment, force, mu)
+    else:
+        sentiment = get_sentimental(sentiment, 0, mu)
+    
+    
+    key = 'sentiment'
+    value = sentiment
+    
+    return (key, value)
+
+def get_sentimental(sentiment, force, decay=0):
+    mu = decay
+    sentiment = sentiment*(1-mu) + force
+    
+    if sentiment > 1:
+        sentiment = 1
+        
+    return sentiment
+
+#functions for partial state update block 3
+
+#Decision processes: trigger function policy
+##-----------------------------------------
+
+def trigger_function(params, step, sL, s):
+    
+    network = s['network']
+    funds = s['funds']
+    supply = s['supply']
+    proposals = get_nodes_by_type(network, 'proposal')
+    tmin = params['tmin']
+    
+    accepted = []
+    triggers = {}
+    for j in proposals:
+        if network.nodes[j]['status'] == 'candidate':
+            requested = network.nodes[j]['funds_requested']
+            age = network.nodes[j]['age']
+            threshold = trigger_threshold(requested, funds, supply)
+            if age > tmin:
+                conviction = network.nodes[j]['conviction']
+                if conviction >threshold:
+                    accepted.append(j)
+        else:
+            threshold = np.nan
+            
+        triggers[j] = threshold
+                
+        
+                    
+    return({'accepted':accepted, 'triggers':triggers})
+
+def decrement_funds(params, step, sL, s, _input):
+    
+    funds = s['funds']
+    network = s['network']
+    accepted = _input['accepted']
+
+    #decrement funds
+    for j in accepted:
+        funds = funds - network.nodes[j]['funds_requested']
+    
+    key = 'funds'
+    value = funds
+    
+    return (key, value)
+
+def update_proposals(params, step, sL, s, _input):
+    
+    network = s['network']
+    accepted = _input['accepted']
+    triggers = _input['triggers']
+    participants = get_nodes_by_type(network, 'participant')
+    proposals = get_nodes_by_type(network, 'proposals')
+    sensitivity = params['sensitivity']
+    
+    for j in proposals:
+        network.nodes[j]['trigger'] = triggers[j]
+    
+    #bookkeeping conviction and participant sentiment
+    for j in accepted:
+        network.nodes[j]['status']='active'
+        network.nodes[j]['conviction']=np.nan
+        #change status to active
+        for i in participants:
+        
+            #operating on edge = (i,j)
+            #reset tokens assigned to other candidates
+            network.edges[(i,j)]['tokens']=0
+            network.edges[(i,j)]['conviction'] = np.nan
+            
+            #update participants sentiments (positive or negative) 
+            affinities = [network.edges[(i,p)]['affinity'] for p in proposals if not(p in accepted)]
+            if len(affinities)>1:
+                max_affinity = np.max(affinities)
+                force = network.edges[(i,j)]['affinity']-sensitivity*max_affinity
+            else:
+                force = 0
+            
+            #based on what their affinities to the accepted proposals
+            network.nodes[i]['sentiment'] = get_sentimental(network.nodes[i]['sentiment'], force, False)
+            
+    
+    key = 'network'
+    value = network
+    
+    return (key, value)
+
+def update_sentiment_on_release(params, step, sL, s, _input):
+    
+    network = s['network']
+    proposals = get_nodes_by_type(network, 'proposal')
+    accepted = _input['accepted']
+    
+    proposals_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate'])
+    
+    proposals_accepted = np.sum([network.nodes[j]['funds_requested'] for j in accepted])
+    
+    sentiment = s['sentiment']
+    force = proposals_accepted/proposals_outstanding
+    if (force >=0) and (force <=1):
+        sentiment = get_sentimental(sentiment, force, False)
+    else:
+        sentiment = get_sentimental(sentiment, 0, False)
+    
+    key = 'sentiment'
+    value = sentiment
+    
+    return (key, value)
+
+def participants_decisions(params, step, sL, s):
+    network = s['network']
+    participants = get_nodes_by_type(network, 'participant')
+    proposals = get_nodes_by_type(network, 'proposal')
+    candidates = [j for j in proposals if network.nodes[j]['status']=='candidate']
+    sensitivity = params['sensitivity']
+    
+    gain = .01
+    delta_holdings={}
+    proposals_supported ={}
+    for i in participants:
+        force = network.nodes[i]['sentiment']-sensitivity
+        delta_holdings[i] = network.nodes[i]['holdings']*gain*force
+        
+        support = []
+        for j in candidates:
+            affinity = network.edges[(i, j)]['affinity']
+            cutoff = sensitivity*np.max([network.edges[(i,p)]['affinity'] for p in candidates])
+            if cutoff <.5:
+                cutoff = .5
+            
+            if affinity > cutoff:
+                support.append(j)
+        
+        proposals_supported[i] = support
+    
+    return({'delta_holdings':delta_holdings, 'proposals_supported':proposals_supported})
+
+def update_tokens(params, step, sL, s, _input):
+    
+    network = s['network']
+    delta_holdings = _input['delta_holdings']
+    proposals = get_nodes_by_type(network, 'proposal')
+    proposals_supported = _input['proposals_supported']
+    participants = get_nodes_by_type(network, 'participant')
+    alpha = params['alpha']
+    
+    for i in participants:
+        network.nodes[i]['holdings'] = network.nodes[i]['holdings']+delta_holdings[i]
+        supported = proposals_supported[i]
+        total_affinity = np.sum([ network.edges[(i, j)]['affinity'] for j in supported])
+        for j in proposals:
+            if j in supported:
+                normalized_affinity = network.edges[(i, j)]['affinity']/total_affinity
+                network.edges[(i, j)]['tokens'] = normalized_affinity*network.nodes[i]['holdings']
+            else:
+                network.edges[(i, j)]['tokens'] = 0
+            
+            prior_conviction = network.edges[(i, j)]['conviction']
+            current_tokens = network.edges[(i, j)]['tokens']
+            network.edges[(i, j)]['conviction'] =current_tokens+alpha*prior_conviction
+    
+    for j in proposals:
+        network.nodes[j]['conviction'] = np.sum([ network.edges[(i, j)]['conviction'] for i in participants])
+    
+    key = 'network'
+    value = network
+    
+    return (key, value)
+
+def update_supply(params, step, sL, s, _input):
+    
+    supply = s['supply']
+    delta_holdings = _input['delta_holdings']
+    delta_supply = np.sum([v for v in delta_holdings.values()])
+    
+    supply = supply + delta_supply
+    
+    key = 'supply'
+    value = supply
+    
+    return (key, value)
+
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
+# The Partial State Update Blocks
+partial_state_update_blocks = [
+    {
+        'policies': {
+            #new proposals or new participants
+            'random': driving_process
+        },
+        'variables': {
+            'network': update_network,
+            'funds':increment_funds,
+            'supply':increment_supply
+        }
+    },
+    {
+      'policies': {
+          'completion': check_progress #see if any of the funded proposals completes
+        },
+        'variables': { # The following state variables will be updated simultaneously
+            'sentiment': update_sentiment_on_completion, #note completing decays sentiment, completing bumps it
+            'network': complete_proposal #book-keeping
+        }
+    },
+        {
+      'policies': {
+          'release': trigger_function #check each proposal to see if it passes
+        },
+        'variables': { # The following state variables will be updated simultaneously
+            'funds': decrement_funds, #funds expended
+            'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment
+            'network': update_proposals #reset convictions, and participants sentiments
+                                        #update based on affinities
+        }
+    },
+    {
+        'policies': {
+            'participants_act': participants_decisions, #high sentiment, high affinity =>buy
+                                                        #low sentiment, low affinities => burn
+                                                        #assign tokens to top affinities
+        },
+        'variables': {
+            'supply': update_supply,
+            'network': update_tokens #update everyones holdings
+                                    #and their conviction for each proposal
+        }
+    }
+]
+
+n= 25 #initial participants
+m= 3 #initial proposals
+
+initial_sentiment = .5
+
+network, initial_funds, initial_supply, total_requested = initialize_network(n,m,total_funds_given_total_supply,trigger_threshold)
+
+initial_conditions = {'network':network,
+                      'supply': initial_supply,
+                      'funds':initial_funds,
+                      'sentiment': initial_sentiment}
+
+#power of 1 token forever
+# conviction_capactity = [2]
+# alpha = [1-1/cc for cc in conviction_capactity]
+# print(alpha)
+
+params={
+    'sensitivity': [.75],
+    'tmin': [7], #unit days; minimum periods passed before a proposal can pass
+    'sentiment_decay': [.001], #termed mu in the state update function
+    'alpha': [0.5, 0.9],
+    'base_completion_rate': [10],
+    'trigger_func': [trigger_threshold]
+}
+
+
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
+# Settings of general simulation parameters, unrelated to the system itself
+# `T` is a range with the number of discrete units of time the simulation will run for;
+# `N` is the number of times the simulation will be run (Monte Carlo runs)
+time_periods_per_run = 250
+monte_carlo_runs = 1
+
+simulation_parameters = config_sim({
+    'T': range(time_periods_per_run),
+    'N': monte_carlo_runs,
+    'M': params
+})
+
+
+from cadCAD.configuration import append_configs
+
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
+# The configurations above are then packaged into a `Configuration` object
+append_configs(
+    initial_state=initial_conditions, #dict containing variable names and initial values
+    partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions
+    sim_configs=simulation_parameters #dict containing simulation parameters
+)
+
+from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
+from cadCAD import configs
+
+exec_mode = ExecutionMode()
+multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
+run = Executor(exec_context=multi_proc_ctx, configs=configs)
+raw_result, tensor = run.execute()
+
+#import pandas as pd
+#df = pd.DataFrame(raw_result)
+# exec_mode = ExecutionMode()
+# exec_context = ExecutionContext(context=exec_mode.multi_proc)
+# # run = Executor(exec_context=exec_context, configs=configs)
+# executor = Executor(exec_context, configs) # Pass the configuration object inside an array
+# raw_result, tensor = executor.execute() # The `main()` method returns a tuple; its first elements contains the raw results
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