The Effect of Individual and Collective Characteristics on Team Performance: A Model of Networked Agents Engaged in Collective Problem Solving (1.2.0)
            This code is for an agent-based model of collective problem solving in which agents with different behavior strategies, explore the NK landscape while they communicate with their peers agents. This model is based on  the famous work of Lazer, D., & Friedman, A. (2007), The network structure of exploration and exploitation.
            
            Release Notes
            Read me file for the netlogo code 
Resarch Title: The effect of team members’ behaviour on the performance of team’s collective problem solving
Author: Amin Boroomand
Version 2.0
date: 04/22/2020
Quick breakdown of code sections based on order of appearance on the code file:
Definitions: 
    Global variables
    Agents and its properties
Setup function: It is hit when we press set up. It runs the following functions:
    Define-allele-values:
    Set-interdepenencies
    Spawn-turtle
    Wire-ringlat
Run_step: this function does the exploration and exploitation for each agent in each tick
    Exploitation: fiDefinitionsrst it checks if any of the neighbor agents of any of them have a better solution. If so, it adops the better solution, otherwise it moves to to exploration
    Exploration: depending on the type of the agent, the exploration behavior is defined.
Note: run_step function uses to functions:
    Explore: this function randomly changes one of bites of the solution and returns a proposed new solution. It does not automatically update the agent’ current solution. 
    Evaluate: this function calculates the score associated to each solution
The rest of the functions are used to define the space and in the functions inside the setup function
            Associated Publications
            Boroomand, A., & Smaldino, P. E. (2021). Hard Work, Risk-Taking, and Diversity in a Model of Collective Problem Solving. Journal of Artificial Societies and Social Simulation, 24(4).
         
    
    
        
        
            
        
        The Effect of Individual and Collective Characteristics on Team Performance: A Model of Networked Agents Engaged in Collective Problem Solving 1.2.0
        
            
                Submitted by
                
                    Amin Boroomand
                
            
            
                
                    Published Jul 26, 2021
                
            
            
                Last modified May 01, 2022
            
         
        
        
            
                This code is for an agent-based model of collective problem solving in which agents with different behavior strategies, explore the NK landscape while they communicate with their peers agents. This model is based on  the famous work of Lazer, D., & Friedman, A. (2007), The network structure of exploration and exploitation.
             
            
                
            
            
            Release Notes
            
                
Read me file for the netlogo code 
Resarch Title: The effect of team members’ behaviour on the performance of team’s collective problem solving
Author: Amin Boroomand
Version 2.0
date: 04/22/2020
Quick breakdown of code sections based on order of appearance on the code file:
Definitions: 
    Global variables
    Agents and its properties
Setup function: It is hit when we press set up. It runs the following functions:
    Define-allele-values:
    Set-interdepenencies
    Spawn-turtle
    Wire-ringlat
Run_step: this function does the exploration and exploitation for each agent in each tick
    Exploitation: fiDefinitionsrst it checks if any of the neighbor agents of any of them have a better solution. If so, it adops the better solution, otherwise it moves to to exploration
    Exploration: depending on the type of the agent, the exploration behavior is defined.
Note: run_step function uses to functions:
    Explore: this function randomly changes one of bites of the solution and returns a proposed new solution. It does not automatically update the agent’ current solution. 
    Evaluate: this function calculates the score associated to each solution
The rest of the functions are used to define the space and in the functions inside the setup function