TABLE 4.

Other markets

Name/focus	Description	Results/findings	References
Labor market	A model of the labor market where agents use energy to find jobs by direct job search or making friends who would tell them about jobs. Agents who run out of energy will die and agents who gain sufficient energy will reproduce by cloning another agent who has similar social networks and search strategies.	The model discovers individual robustness for surviving job losses can result in overall loss in population efficiency, with implications for new policies that could be implemented.	(Tassier & Menczer, 2001)
Electricity trading	An evolutionary model of a detailed, plant‐by‐plant simulation of the new electricity trading arrangements (NETA) in the UK where agents learn and modify their actions with the purpose of maximizing their own profit.	The model was able to make predictions for pricing and strategic insights relevant to NETA.	(Bunn & Oliveira, 2001)
Wholesale electricity market	An agent‐based model of a wholesale electricity market where electricity pricing is determined by a clearinghouse double auction, where buyers and sellers participate repeatedly in auction rounds.	High market efficiency is achieved and market microstructure is predictive of the relative market powers of buyers and sellers.	(Nicolaisen et al., 2001)
Fish market	A model of a wholesale fish market where sellers decide on supply quantity, asking price, and how to treat loyal customers, using evolutionary algorithms such as genetic algorithm, hill climbing, and annealing schedules, and buyers decide on who to buy from and the prices they would accept.	The model explains both stylized facts price dispersion and high loyalty, it also explains the effect of heterogeneity of the buyers.	(Kirman & Vriend, 2001)
Labor productivity	An evolutionary agent‐based model of output‐ and labor‐market dynamics where firms, using labor as the sole input under a constant returns to scale regime, produce a homogeneous, perishable good. Labor productivities are specific to each firm and alter stochastically as a result of technical progress. The model allows for a process in which firms are chosen based on their demonstrated competitiveness.	The model generates predictions about how system parameters affect aggregate performance and its volatility.	(Fagiolo et al., 2004)
Consumer market	An artificial world was developed to investigate the effects of organizational learning strategies on consumer market share.	The simulation demonstrated that different learning strategies work better depending on the environment and timing.	(Lu et al., 2008)