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Agent-Based Modeling (ABM) is a computational method used to examine social outcomes emerging from interaction between heterogeneous agents by computer simulation. It can be used to understand the effect of initial conditions on complex outcomes by exploring fine-grained (multiple-scale, spatial/temporal) observations on the aggregate consequences of agent interaction. By performing in silico experimental tests on policy interventions where ex ante predictions of outcomes are difficult, it can also reduce costs, explore assumptions and boundary conditions, as well as overcome ethical constraints associated with the use of randomized controlled trials in behavioral policy. Here, we introduce the essential elements of ABM and present two simple examples where we assess the hypothetical impact of certain policy interventions while considering different possible reactions of individuals involved in the context. Although highly abstract, these examples suggest that ABM can be either a complement or an alternative to behavioral policy methods, especially when understanding social processes and exploring direct and indirect effects of interventions are important. Prospects and critical problems of these in silico policy experiments are then discussed.

Neuro-symbolic integration is a current field of investigation in which symbolic approaches are combined with deep learning ones. In this work we start from simple non-relational knowledge that can be extracted from text by considering the co-occurrence of entities inside textual corpora; we show that we can easily integrate this knowledge with Logic Tensor Networks (LTNs), a neuro-symbolic model. Using LTNs it is possible to integrate axioms and facts with commonsense knowledge represented in a sub-symbolic form in one single model performing well in reasoning tasks. In spite of some current limitations, we show that results are promising.

Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help users explore information extracted from a KG, including SAs, while they are reading an input text. Because of the large number of SAs that can be extracted from a text, a first challenge in these applications is to effectively determine which SAs are most interesting to the users, defining a suitable ranking function over SAs. However, since different users may have different interests, an additional challenge is to personalize this ranking function to match individual users’ preferences. In this paper we introduce a novel active learning to rank model to let a user rate small samples of SAs, which are used to iteratively learn a personalized ranking function. Experiments conducted with two data sets show that the approach is able to improve the quality of the ranking function with a limited number of user interactions.

DaCENA (Data Context for News Articles) is a web application that showcases a new approach to reading online news articles with the support of a data context built from interlinked facts available on the Web of Data. Given a source article, a set of facts that are estimated to be more interesting for the readers are extracted from the Web and presented using tailored information visualization methods and an interactive user interface. By looking at this background factual knowledge, the reader is supported in the interpretation of the news content and is suggested connections to related topics that he/she can further explore.