Establishing credibility and trustworthiness is essential in Economic Agent-Based Modeling (ABM), where clear epistemic standards cannot be defined a priori. In this paper, we first review the notions of trustworthiness and credibility in modeling. We then introduce a framework that emphasizes the modeler’s epistemic responsibility to ensure coherence between modeling purposes, strategies and targets. We examine the challenges in assessing model reliability that arise from the interaction of conceptual, algorithmic and computational constituents, and we propose a meta-analytical approach to enhance model consistency by conceptualizing ABMs as iterated analogies. Our analysis outlines strategies for improving model accessibility and reliability while highlighting the modeler’s role in preventing mistargeting and misuse. This research provides a normative basis for justifying the credibility of both idealized and targetless models by promoting transparency and consistency between model design and intended purposes.

Agent-based modeling (ABM) is a simulation technique which has been increasingly integrated into the economic discipline in order to understand complex systems. However, most of everyday research activities rely on the researchers' consensus concerning practical choices about modeling strategies, computational boundaries under scrutiny and the extent of empirical validation. Particularly lacking are reflections on the semantic construction of conceptual models. The paper reviews existing theoretical frameworks leading to understanding ABM as a technique, where the cognitive processing instantiated by the instrument is distributed across different modeling layers, including conceptual, algorithmic and computational ones, which can be interpreted as an interlinked set of analogies. Then, it introduces a framework for assessing ABM conceptual adequacy and tests it on two families of models in the field of economics of innovation, revealing several modeling constraints.

We consider measures of covering and separation that are expressed through maxima and minima of distances between points of an hypersphere. We investigate the behavior of these measures when applied to a sample of independent and uniformly distributed points. In particular, we derive their asymptotic distributions when the number of points diverges. These results can be useful as a benchmark against which deterministic point sets can be evaluated. Whenever possible, we supplement the rigorous derivation of these limiting distributions with some heuristic reasonings based on extreme value theory. As a by-product, we provide a proof for a conjecture on the hole radius associated to a facet of the convex hull of points distributed on the hypersphere.

We consider the estimation of the entropy of a discretely-supported time series through a plug-in estimator. We provide a correction of the bias and we study the asymptotic properties of the estimator. We show that the widely-used correction proposed by Roulston (1999) is incorrect as it does not remove the $O\left(N^{-1}\right)$ part of the bias while ours does. We provide the asymptotic distribution and we show that it differs when the values taken by the marginal distribution of the process are equiprobable (a situation that we call degeneracy) and when they are not. We introduce estimators of the bias, the variance and the distribution under degeneracy and we study the estimation error. Finally, we propose a goodness-of-fit test based on entropy and give two motivations for it. The theoretical results are supported by specific numerical examples.