text: import numpy as np
  """
  Skeleton code for the process implemented on a fully connected network.
  """
  
  # load FP-sim data for calculations
  
  # class for agents
  
  # initialize network and agents
  theta_plus = 3
  theta_minus = -3
  num_agents = 10
  A = np.ones((num_agents, num_agents))
  
  def information(time, x_0, x_1):
  	# compute the information gained from knowing
  	# an agent's LLR lies between x_0 and x_1
      return B
  
  # start independent accumulation
  first_decider = 0
  while first_decider == 0:
  	# go through agents and add observations
  	# check if any agent has decided
  	if decided == 1:
  		first_decider = 1
  
  # add decider amount to all agents
  # split into sets A and D
  # agents in A make decision
  # compute the increment for the agents in D
  # check for trivial cases
  # see if any agents have decided and remove them from the remaining agents
  # increment agents
  # continue process (which can take up to |D| + 2 steps and may require repeated use of the information function
  
  #return agent decisions, LLR plot, decision times, and correctness
  	
  