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 driving_process(params, step, sL, s):
#placeholder plumbing for random processes
arrival_rate = 10 / (1 + s['sentiment'])
rv1 = np.random.rand()
new_participant = bool(rv1 < 1 / arrival_rate)
supporters = get_edges_by_type(s['network'], 'support')
len_parts = len(get_nodes_by_type(s['network'], 'participant'))
supply = s['supply']
expected_holdings = .1 * supply / len_parts
if new_participant:
h_rv = expon.rvs(loc=0.0, scale=expected_holdings)
new_participant_holdings = h_rv
else:
new_participant_holdings = 0
network = s['network']
affinities = [network.edges[e]['affinity'] for e in supporters]
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 = 1 / median_affinity * (1 + total_funds_requested / funds)
rv2 = np.random.rand()
new_proposal = bool(rv2 < 1 / proposal_rate)
sentiment = s['sentiment']
funds = s['funds']
scale_factor = funds * sentiment**2 / 10000
if scale_factor < 1:
scale_factor = 1
#this shouldn't happen but expon is throwing domain errors
if sentiment > .4:
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
})
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 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']
trigger_func = params['trigger_func']
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_func(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 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 update_network(params, step, sL, s, _input):
network = s['network']
funds = s['funds']
supply = s['supply']
trigger_func = params['trigger_func']
#print(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 gen_new_proposal(network, funds, supply, total_funds, 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 / total_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 update_tokens(params, step, sL, s, _input):
network = s['network']
delta_holdings = _input['delta_holdings']
proposals = get_nodes_by_type(network, 'proposal')
candidates = [
j for j in proposals if network.nodes[j]['status'] == 'candidate'
]
proposals_supported = _input['proposals_supported']
participants = get_nodes_by_type(network, 'participant')
alpha = params['alpha']
min_support = params['min_supp']
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 candidates:
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 candidates:
network.nodes[j]['conviction'] = np.sum(
[network.edges[(i, j)]['conviction'] for i in participants])
total_tokens = np.sum(
[network.edges[(i, j)]['tokens'] for i in participants])
if total_tokens < min_support:
network.nodes[j]['status'] = 'killed'
key = 'network'
value = network
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:
engagement_rate = .3 * network.nodes[i]['sentiment']
if np.random.rand() < engagement_rate:
force = network.nodes[i]['sentiment'] - sensitivity
delta_holdings[i] = network.nodes[i]['holdings'] * gain * force
support = []
for j in candidates:
booster = social_affinity_booster(network, j, i)
#print(booster)
affinity = network.edges[(i, j)]['affinity'] + booster
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
else:
delta_holdings[i] = 0
proposals_supported[i] = [
j for j in candidates if network.edges[(i, j)]['tokens'] > 0
]
return ({
'delta_holdings': delta_holdings,
'proposals_supported': proposals_supported
})
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 complete_proposal(params, step, sL, s, _input):
network = s['network']
participants = get_nodes_by_type(network, 'participant')
proposals = get_nodes_by_type(network, 'proposal')
competitors = get_edges_by_type(network, 'conflict')
completed = _input['completed']
for j in completed:
network.nodes[j]['status'] = 'completed'
for c in proposals:
if (j, c) in competitors:
conflict = network.edges[(j, c)]['conflict']
for i in participants:
network.edges[(i, c)]['affinity'] = network.edges[
(i, c)]['affinity'] * (1 - conflict)
for i in participants:
force = network.edges[(i, j)]['affinity']
sentiment = network.nodes[i]['sentiment']
network.nodes[i]['sentiment'] = get_sentimental(sentiment,
force,
decay=0)
failed = _input['failed']
for j in failed:
network.nodes[j]['status'] = 'failed'
for i in participants:
force = -network.edges[(i, j)]['affinity']
sentiment = network.nodes[i]['sentiment']
network.nodes[i]['sentiment'] = get_sentimental(sentiment,
force,
decay=0)
key = 'network'
value = network
return (key, value)
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})
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
})
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 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']
trigger_func = params['trigger_func']
accepted = []
triggers = {}
funds_to_be_released = 0
for j in proposals:
if network.nodes[j]['status'] == 'candidate':
requested = network.nodes[j]['funds_requested']
age = network.nodes[j]['age']
threshold = trigger_func(requested, funds, supply)
if age > tmin:
conviction = network.nodes[j]['conviction']
if conviction > threshold:
accepted.append(j)
funds_to_be_released = funds_to_be_released + requested
else:
threshold = np.nan
triggers[j] = threshold
#catch over release and keep the highest conviction results
if funds_to_be_released > funds:
#print('funds ='+str(funds))
#print(accepted)
ordered = conviction_order(network, accepted)
#print(ordered)
accepted = []
release = 0
ind = 0
while release + network.nodes[
ordered[ind]]['funds_requested'] < funds:
accepted.append(ordered[ind])
release = network.nodes[ordered[ind]]['funds_requested']
ind = ind + 1
return ({'accepted': accepted, 'triggers': triggers})
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
