def update_threshold(self, funding_pool: float, token_supply: float, max_proposal_request: float):
if self.status == ProposalStatus.CANDIDATE:
self.trigger = trigger_threshold(
self.funds_requested, funding_pool, token_supply, max_proposal_request)
else:
self.trigger = np.nan
return self.trigger
def su_add_to_network(params, step, sL, s, _input, **kwargs):
network = s["network"]
funding_pool = s["funding_pool"]
token_supply = s["token_supply"]
gamma_func = params["gamma_func"]
random_number_func = params["random_number_func"]
if _input["new_proposal"]:
proposed_by = _input["proposed_by_participant"]
# Create the Proposal and add it to the network.
rescale = funding_pool * config.scale_factor
r_rv = gamma_func(config.funds_requested_alpha,
loc=config.funds_requested_min,
scale=rescale)
proposal = Proposal(funds_requested=r_rv,
trigger=trigger_threshold(
r_rv, funding_pool, token_supply,
params["max_proposal_request"]))
network, proposal_idx = add_proposal(network, proposal,
proposed_by,
random_number_func)
if params.get("debug"):
print(
"GenerateNewProposal: Participant {} created Proposal {}".
format(_input["proposed_by_participant"], proposal_idx))
return "network", network
def add_proposals_and_relationships_to_network(
n: nx.DiGraph, proposals: int, funding_pool: float,
token_supply: float) -> nx.DiGraph:
participant_count = len(n)
for i in range(proposals):
j = participant_count + i
n.add_node(j, type="proposal", conviction=0, status="candidate", age=0)
r_rv = gamma.rvs(3, loc=0.001, scale=10000)
n.nodes[j]['funds_requested'] = r_rv
n.nodes[j]['trigger'] = trigger_threshold(r_rv, funding_pool,
token_supply)
for i in range(participant_count):
n.add_edge(i, j)
rv = np.random.rand()
a_rv = 1 - 4 * (1 - rv) * rv #polarized distribution
n.edges[(i, j)]['affinity'] = a_rv
n.edges[(i, j)]['tokens'] = 0
n.edges[(i, j)]['conviction'] = 0
n.edges[(i, j)]['type'] = 'support'
# Conflict Rate is a potential variable to optimize
# Relative Influence is a potential variable to optimize
n = initial_conflict_network(n, rate=.25)
n = initial_social_network(n, scale=1)
return n
def su_add_to_network(params, step, sL, s, _input, **kwargs):
network = s["network"]
funding_pool = s["funding_pool"]
token_supply = s["token_supply"]
scale_factor = 0.01
if _input["new_proposal"]:
# Create the Proposal and add it to the network.
rescale = funding_pool * scale_factor
r_rv = gamma.rvs(3, loc=0.001, scale=rescale)
proposal = Proposal(funds_requested=r_rv,
trigger=trigger_threshold(
r_rv, funding_pool, token_supply,
params["max_proposal_request"]))
network, proposal_idx = add_proposal(network, proposal)
# add_proposal() has created support edges from other Participants
# to this Proposal. If the Participant is the one who created this
# Proposal, change his affinity for the Proposal to 1 (maximum).
network.edges[_input["proposed_by_participant"],
proposal_idx]["affinity"] = 1
if params.get("debug"):
print(
"GenerateNewProposal: Participant {} created Proposal {}".
format(_input["proposed_by_participant"], proposal_idx))
return "network", network
def su_add_to_network(params, step, sL, s, _input, **kwargs):
network = s["network"]
funding_pool = s["funding_pool"]
token_supply = s["token_supply"]
gamma_func = params["gamma_func"]
random_number_func = params["random_number_func"]
if _input["new_proposal"]:
# Create the Proposal and add it to the network.
rescale = funding_pool * config.scale_factor
r_rv = gamma_func(config.funds_requested_alpha,
loc=config.funds_requested_min,
scale=rescale)
proposal = Proposal(funds_requested=r_rv,
trigger=trigger_threshold(
r_rv, funding_pool, token_supply,
params["max_proposal_request"]))
network, proposal_idx = add_proposal(network, proposal,
random_number_func)
# add_proposal() has created support edges from other Participants
# to this Proposal. If the Participant is the one who created this
# Proposal, set the participant's role as author and change his affinity for the Proposal to 1 (maximum).
network.edges[_input["proposed_by_participant"],
proposal_idx]["support"] = \
network.edges[_input["proposed_by_participant"], proposal_idx]["support"]._replace(affinity=1, is_author=True)
if params.get("debug"):
print(
"GenerateNewProposal: Participant {} created Proposal {}".
format(_input["proposed_by_participant"], proposal_idx))
return "network", network
def has_enough_conviction(self, funding_pool: float, token_supply: float, max_proposal_request):
"""
It's just a conviction < threshold check, but we recalculate the
trigger_threshold so that the programmer doesn't have to remember to run
update_threshold before running this method.
"""
if self.status == ProposalStatus.CANDIDATE:
threshold = trigger_threshold(
self.funds_requested, funding_pool, token_supply, max_proposal_request)
if self.conviction < threshold:
return False
return True
else:
raise(Exception(
"Proposal is not a Candidate Proposal and so asking it if it will pass is inappropriate"))
def bootstrap_network(n_participants: List[TokenBatch], n_proposals: int, funding_pool: float, token_supply: float, max_proposal_request: float) -> nx.DiGraph:
"""
Convenience function that creates a network ready for simulation in
the Python notebook in one line.
"""
n = create_network(n_participants)
for _ in range(n_proposals):
idx = len(n)
r_rv = gamma.rvs(3, loc=0.001, scale=10000)
n.add_node(idx, item=Proposal(funds_requested=r_rv, trigger=trigger_threshold(
r_rv, funding_pool, token_supply, max_proposal_request)))
n = setup_support_edges(n)
n = setup_conflict_edges(n)
n = setup_influence_edges_bulk(n)
return n
def bootstrap_network(n_participants: List[TokenBatch], n_proposals: int, funding_pool: float, token_supply: float, max_proposal_request: float, probability_func, random_number_func, gamma_func, exponential_func) -> nx.DiGraph:
"""
Convenience function that creates a network ready for simulation in
the Python notebook in one line.
"""
n = create_network(n_participants, probability_func, random_number_func)
for _ in range(n_proposals):
idx = len(n)
r_rv = gamma_func(3, loc=0.001, scale=10000)
n.add_node(idx, item=Proposal(funds_requested=r_rv, trigger=trigger_threshold(
r_rv, funding_pool, token_supply, max_proposal_request)))
n = setup_support_edges(n, random_number_func)
n = setup_conflict_edges(n, random_number_func)
# n = setup_influence_edges_bulk(n, exponential_func) # TODO: Disabled as these aren't being used on any model policy
return n
def su_add_to_network(params, step, sL, s, _input):
network = s["network"]
funding_pool = s["funding_pool"]
token_supply = s["token_supply"]
scale_factor = 0.01
if _input["new_proposal"]:
# Create the Proposal and add it to the network.
rescale = funding_pool * scale_factor
r_rv = gamma.rvs(3, loc=0.001, scale=rescale)
proposal = Proposal(funds_requested=r_rv,
trigger=convictionvoting.trigger_threshold(r_rv, funding_pool, token_supply))
network, j = add_proposal(network, proposal)
# add_proposal() has created support edges from other Participants
# to this Proposal. If the Participant is the one who created this
# Proposal, change his affinity for the Proposal to 1 (maximum).
network.edges[_input["proposed_by_participant"], j]["affinity"] = 1
return "network", network
def test_small_proposal_should_have_low_threshold(self):
threshold = trigger_threshold(10, 1000, 10000000, 0.2)
# This number is not special, just used to make sure everything stays the same
self.assertEqual(threshold, 5540166.20498615)
def test_small_proposal_should_have_low_threshold(self):
threshold = trigger_threshold(10, 1000, 10000000)
print(threshold)