Assessment details

- All students will develop their own ABM in NetLogo and perform some experiments and analysis. The program should contain appropriate comments in the code, user interface with input parameters and outputs and a completed “info” screen in similar format to the models in the NetLogo models library.
- PhD students will additionally produce a short technical report describing some experimental results from their model. This should include: 1) abstract; 2) introduction; 3) description of model; 4) experiments performed and results (which may include charts, figures and / or tables); and 5) discussion / conclusion. Minimum 3 pages. Note: Do not worry if your model does not do what you thought or hoped it would, you just need to described what you did and what you found.

- Topic (one short
paragraph) to
be e-mailed to dave@davidhales.com before 3 May
(lab 9) [All students]

- ABM model deadline: 14 June (email the .nlogo file). [All students]
- Technical report deadline: 28 June (email a .pdf file). [PhD students only]

Implement a model described in a paper and check if you get similar results. Find a paper that describes an ABM and attempt to re-implement it. Do you get the same (or similar) results as reported in the paper?

Produce a variation on an existing ABM in the NetLogo models library. Incoporate agents with some new capabilities / behaviours. How do the new capabilities change the outcomes of the model (if at all). Can you explain the results?

How can network topologies self-organise? Suppose you wished agents to dynamically form various network topologies without a top-down plan. Assume agents (nodes) can not access a unique ordered ID (the who number in NetLogo) but rather all start identically as entirely disconnected nodes. Each node only has access to it's own and other nodes number of links and (perhaps) some other internal state variables that are updated in a local way (for example you could store the age of a link). The preferential attachment algorithm creates a scale-free network only using the number of links of each node. Are there agent behavours that could produce rings, stars or lattice like structures in a similar decentralised way?

How can we keep a network connected with only local queries? Make a model of a dynamic network (graph) in which there are on average N nodes. Existing nodes leave and new nodes join with probability P each time step. New nodes connect to a randomly selected single existing node. Nodes can only query nodes they are directly connected to and can only store a small number of links to other nodes (K). Queries involve asking another node for its current links. Implement some simple node behaviours that try to keep the network connected while minimising the number of queries. Test with different values of N, P and K.

How do ideas spread through networks? Implement a population of N agents connected in a network. Each agent can have one of two opinions (red or blue). Agents change their opinion if > T proportion of their neighbours hold a different opinion. Starting from a condition in which all agents share the same opinion explore the conditions that allow a single node changing it’s opinion to spread over the entire network. Experiment with different network topologies and T values.

How does social learning effect a coordination game? Make a variation of the El Farol Bar model in which there are four kinds of learners: 1) individual learners (as in the standard model); 2) social learners that copy the best strategies from other agents rather than learn themselves; 3) mixed learners who learn themselves and copy from others. Compare results from populations composed entirely of each type and some mixtures of types.

How does assimilation and change effect segregation? Make a variation of the Schelling segregation model in which there are two additional parameters: 1) a second threshold C above the existing T threshold (C > T). If C holds then rather than move, the agent “converts” to a randomly selected neighbour colour. 2) a probability M. In each time step each agent spontaneously changes to a random colour with probability M. Experiment with different numbers of colours and C, M, T and agent density values. How do these effect segregation outcomes? Additionally measure the largest and smallest cluster that emerge in stable segregation outcomes.

How do different topologies effect segregation? Make a variation of Schelling’s segregation model in which agents are located on some other graph topologies than a lattice. Experiment with a number of different graph topologies, threshold values, number of colours and densities. How do these parameters effect the emergence of segregation? In addition consider a 3D lattice and some form of dynamic topologies (where the graph changes over time).

Kill the things (in space)! The universe is a 2D space. You are in a spaceship that can only be rotated. It can fire torpedoes from the front. Things are trying to get you. If they hit you then you die. If you shoot one with a torpedo they die. Things store a movement rule that determines how fast and in what way they move. Periodically things hatch a new thing with probability P. New things are copies of their parent but with probability M they mutate their movement rule in some way. If all things are killed a set of new things appear with completely random movement rules. The user can rotate the spaceship, left or right, and fire by pressing keys. Additionally add an autopilot function that controls the spaceship attempting to kill things without user intervention. How does the behaviour of things evolve with different P and M values?

How do dynamic graphs effect cooperation? Produce an evolutionary model of agents playing a simple cooperation game (such as the single-round Prisoner’s Dilemma or similar) on a dynamic graph. The graph could change following some known growth rule (such as preferential attachment), some dynamic change (such as random rewiring) or any other mechanism. Parameterise the rate of change (R) of the graph and the mutation rate (M) of the strategies and explore different values for R and M in relation to cooperation level.

How do different fixed strategies in repeated cooperation games compare? Implement several (at least 5) different strategies in the Iterated Prisoner’s Dilemma game. Implement a “round robin” tournament in which all possible pairs of strategies play a game. Present results of all games (a symmetric matrix). Vary the number of games played G and the number of rounds in each game R. How do these effect the results?

How do repeated cooperation strategies evolve? Implement an evolutionary model of a population of agents playing the Iterated Prisoner’s Dilemma. Each agent should store 3 values describing it’s strategy <p,q,r>. Where p is the probability an agent will cooperate on the first move, q is the probability to cooperate if the other player defected last move and r is the probability to cooperate if the other player cooperated last move. Hence <1,0,1> would equate to the tit-for-tat strategy, <0,0,0> would equate to always defect and <1,1,1> would be always cooperate. Experiment to determine what strategies evolve for some different parameters such as mutation rates, population sizes and number of rounds played between partners and different PD payoffs.

How do strategies in the rock / paper / scissors game evolve? Implement a population of N agents who play one strategy in the game rock, paper or scissors. Each time step each agent plays P other randomly chosen agents obtaining a payoff of 1 for a win, zero for a draw and -1 for a loss. After each round of games is played, reproduce some high scoring agents into the next generation and kill some low scoring agents (keeping the population size constant at N). With small probability M mutate (randomly change) the strategy of newly reproduced agents. Experiment with different P, M and N. Do the parameters effect the distribution of strategies over time?