Abstract:
The main purpose of this research is to assess how systemic complexity a ects performance and learning in interactive simulation games. We design experiments involving two types of games: a growth management game, and a stock management game. In the rst stage, we focus on explaining the overall game complexity, in terms of three factors: delay, nonlinearity, and feedback. The growth management game experiments show that the analyzed complexity factors do not necessarily make the game more complex. Also, the e ect of repeated trials (learning) is observed only in some games. The growth game experiments also suggest that complexity factors can limit procedural learning by repeated games, or even induce \false" learning. In the stock management game experiments, only the delay factor creates worsening in game performances. All subject groups exhibit learning by repeated trials. Nevertheless, there is also evidence that delay prevents transfer of learning. In the second stage, the interaction e ects between the complexity factors are tested in both games. The results show that interaction of complexity factors can amplify or dampen their individual e ects. The thesis also compares the performances of some heuristic decision rules with experimental results. A method is presented for obtaining statistical distribution of scores resulting from a given simulated decision heuristic, which can be used to compare against and assess experimental gaming results. We observe that under some complexity conditions, human subjects do not perform better than the random heuristic primitive rule composed of a sequence of random decisions. In the nal part, we test the e ectiveness of a gradual-increase-in-complexity approach on learning. Experiments with three di erent increase-in-complexity procedures show that a gradual increase in complexity does not yield better performances compared to nonincreasing complexity sequences. Probable factors behind these results are discussed. In depth analysis of factors causing these results is a potential further research topic.
