Agent-Based Computational Economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.

Paradigms, possibilities and probabilities: Comment on Hinterecker et al. (2016)

Hinterecker et al. (2016) compared the adequacy of the probabilistic new paradigm in reasoning with the recent revision of mental models theory (MMT) for explaining a novel class of inferences containing the modal term “possibly”. For example, the door is closed or the window is open or both, therefore, possibly the door is closed and the window is open (A or B or both, therefore, possibly(A & B)). They concluded that their results support MMT. In this comment, it is argued that Hinterecker et al. (2016) have not adequately characterised the theory of probabilistic validity (p-validity) on which the new paradigm depends. It is unclear how p-validity can be applied to these inferences, which are anyway peripheral to the theory. It is also argued that the revision of MMT is not well motivated and its adoption leads to many logical absurdities. Moreover, the comparison is not appropriate because these theories are defined at different levels of computational explanation. In particular, revised MMT lacks a provably consistent computational level theory that could justify treating these inferences as valid. It is further argued that the data could result from the non-colloquial locutions used to express the premises. Finally, an alternative pragmatic account is proposed based on the idea that a conclusion is possible if what someone knows cannot rule it out. This account could be applied to the unrevised mental model theory rendering the revision redundant

Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

Implementation Considerations for Mitigating Bias in Supervised Machine Learning

Machine Learning (ML) is an important component of computer science and a mainstream way of making sense of large amounts of data. Although the technology is establishing new possibilities in different fields, there are also problems to consider, one of which is bias. Due to the inductive reasoning of ML algorithms in creating mathematical models, the predictions and trends found by the models will never necessarily be true – just more or less probable. Knowing this, it is unreasonable for us to expect the applied deductive reasoning of these models to ever be fully unbiased. Therefore, it is important that we set expectations for ML that account for the limitations of reality. The current conversation of ML regards how and when to implement the technology to mitigate the effect of bias on its results. This thesis suggests that the question of “whether” should be addressed first. We tackle the issue of bias from the standpoint of justice and fairness in ML, developing a framework tasked with determining whether the implementation of a specific ML model is warranted. We accomplish this by emphasizing the liberal values that drive our definitions of societal fairness and justice, such as the separateness of persons, moral evaluation, freedom and understanding of choice, and accountability for wrongdoings

BEING PROFILED:COGITAS ERGO SUM

Profiling the European citizen: why today's democracy needs to look harder at the negative potential of new technology than at its positive potential