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