## 2 Agent-Based Modelling and Simulation  
In [6] the authors apply _genetic programming_ techniques to an agent-based model of training, education, and entertainment applications with the aim of automatically generating _agent behaviour trees_. By acknowledging shortcomings of genetic programming application to evolve behaviour trees--such as search space explosion and intensive knowledge engineering efforts--they complement the approach with both static and dynamic constraints to ease exploration of the search space while still being fairly domain-independent. To demonstrate efficacy of the proposal, they experiment with behaviour generation for the Pac-man game, achieving better behaviour in fewer evolutionary rounds w.r.t. other state-of-art approaches. As a bonus, they also get more readable behaviour specifications, suitable to be later refined by domain experts. In [7] the author applies agent-based simulation to achieve fair purchase prices in the context of perishable goods markets. They aim at reforming the current market model, in which sellers are penalised by the rapid perishing of their goods (i.e., fish and vegetables) which forces them to accept buyers offers sooner or later--otherwise the goods will be wasted. To overcome this issue, they propose a _double auction model_ in which buyers are penalised each time they fail an offer, so as to promote fair bidding prices. By simulating traders in different market conditions they show that their approach achieves fair prices for the allocation of goods. In [8] the authors developed an agent-based _simulation software tool_ to investigate how _students' sociograms_--a representation of social relationships--evolve during time. It has been demonstrated, in fact, that different sociograms contribute in different ways to the academic performance of students, thus arranging classes and lectures so as to promote such sociograms would be beneficial. Nevertheless, knowing the sociogram beforehand--before the education period starts--is not possible. The authors' work overcomes this limitation by enabling educators to simulate student sociograms as an agent-based model built out of students' psychological profile. The authors corroborate the hypothesis that simulated sociograms are sufficiently close to real ones through statistical binomial testing. In [9] the authors propose TELEKA, an _agent-based model and architecture_ for _network traffic_ analysis and optimisation. The model is intended to exploit _negotiation_ techniques to advance network management practices towards the fifth level of IBM's degree of automaticity in network management [10], which fosters network policies and goals able to autonomously adapt to the contingencies arising during operation. The proposed architecture is vertically decomposed in three layers: the lower one is in charge of fine-grained monitoring and low-level control of individual devices, the middle one is devoted to classify and aggregate perceptions coming from the monitoring so as to deliver higher level information to the decision making module, that is, the upper level of the architecture, in charge of triggering the SEHA negotiation algorithm for congestion resolution and traffic optimisation based on information and alerts coming from the lower levels. Effectiveness of the model is evaluated through simulations in NetLogo ([[https://cl.northwestern.edu/netlogo/](https://cl.northwestern.edu/netlogo/)]([https://cl.northwestern.edu/netlogo/](https://cl.northwestern.edu/netlogo/))), and an analysis of sensitivity to different topologies is included. In [11] the author formally investigates the problem of reaching consensus in presence of either transmission or processing delays. In particular, they focus on _scaled consensus_, where consensus is not about reaching agreement on an absolute common value, but rather about achieving given relative proportions [12].