In this problem set you will apply the solution presented in
num-agents be the number of agents. Each agent
is randomly give two skills from a fixed list of 10 skills. At
each time step a number of new workflows appears. This number is
taken from a Poisson random distribution with mean
workflow-arrival-rate. Each workflow contains a set
of 5 randomly generated skills (repetitions are allowed) and a
payment which is taken from a normal distribution with mean
mean-payment. In order to perform (enact) a
workflow, we need to find 5 agents with the appropriate
skills. An agent can only perform 1 skill for 1 workflow at each
The system must allocate as many of the workflows as possible. The agents, on the other hand, want to maximize their own payments. The workflow payments are divided equally among the agents in a workflow. Since all our workflows have 5 skills, this means that for a workflow of payment p each agent would receive a payment of p/5. The optimal solution maximizes the sum of the payments given to the agents, at every time step.
We modify the annealing mediator solutions, as presented in the paper above (Section 5), as follows (We do this to handle more than 2 agents) . Once a new set of workflows appears we begin a new negotiation. At each negotiation time step a mediator chooses an allocation of agents to workflows (note that there might not be enough agents or workflows, so some workflows might not get enacted or some agents might be idle). It broadcasts this allocation to all and they all give it one of their four votes: strong accept, weak accept, weak reject, strong reject. The mediator converts these votes into numbers using the values
The process ends when the temperature is low enough, at which point we say that the workflows have been allocated.
An agent determines its vote by first calculating the highest payment it could get, which is 1/5 of the payment of the highest-paid workflow that includes one of the agent's skills. Let this payment be maxp. Let p be the agent's payment under the proposed allocation. The truth-telling agent's vote is then decided as follows:
The new problem raises two immediate questions.
The second question is easier to answer since it only requires you to compare the payments accrued by the various types of agents. You will run tests to show the payments accrued in situations where
run-tests) which runs the set of tests and prints out the final data. These tests might take a while to run as you will be running many negotiations.
Finally, note that
mean-payment should be sliders in your model.
I cannot think of a good way of using the turtle space to
visualize the negotiation process. If you can, that would be
great! Otherwise just use the
plot command to draw
some graphs representing the payment distribution among the
agents and the accumulated payment in each run.
As will all the problem sets, you will hand them in using our department's dropbox. You will only hand in your .nlogo file. You will place all your documentation in the documentation tab of your NetLogo model. The information tab needs to start with:
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