Despite the increasing use of standards for documenting and testing agent-based models (ABMs) and sharing of open access code, most ABMs are still developed from scratch. This is not only inefficient, but also leads to ad hoc and often inconsistent implementations of the same theories in computational code and delays progress in the exploration of the functioning of complex social-ecological systems (SES). We argue that reusable building blocks (RBBs) known from professional software development can mitigate these issues. An RBB is a submodel that represents a particular mechanism or process that is relevant across many ABMs in an application domain, such as plant competition in vegetation models, or reinforcement learning in a behavioural model. RBBs need to be distinguished from modules, which represent entire subsystems and include more than one mechanism and process. While linking modules faces the same challenges as integrating different models in general, RBBs are “atomic” enough to be more easily re-used in different contexts. We describe and provide examples from different domains for how and why building blocks are used in software development, and the benefits of doing so for the ABM community and to individual modellers. We propose a template to guide the development and publication of RBBs and provide example RBBs that use this template. Most importantly, we propose and initiate a strategy for community-based development, sharing and use of RBBs. Individual modellers can have a much greater impact in their field with an RBB than with a single paper, while the community will benefit from increased coherence, facilitating the development of theory for both the behaviour of agents and the systems they form. We invite peers to upload and share their RBBs via our website - preferably referenced by a DOI (digital object identifier obtained e.g. via Zenodo). After a critical mass of candidate RBBs has accumulated, feedback and discussion can take place and both the template and the scope of the envisioned platform can be improved.

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Despite having set the theoretical ground for explainable systems decades ago, the information system scholars have given little attention to new developments in the decision-making with humans-in-the-loop in real-world problems. We take the sociotechnical system lenses and employ mixed-method analysis of a field intervention to study the machine-learning informed decision-making with interpreted models' outputs. Contrary to theory, our results suggest a small positive effect of explanations on confidence in the final decision, and a negligible effect on the decisions' quality. We uncover complex dynamic interactions between humans and algorithms, and the interplay of algorithmic aversion, trust, experts' heuristic, and changing uncertainty-resolving condititions.@en

The recent surge of interest in algorithmic decision-making among scholars across disciplines is associated with its potential to resolve the challenges common to administrative decision-making in the public sector, such as greater fairness and equal treatment of each individual, among others. However, algorithmic decision-making combined with human judgment may introduce new complexities with unclear consequences. This article offers evidence that contributes to the ongoing discussion about algorithmic decision-making and governance, contextualizing it within a public employment service. In particular, we discuss the use of a decision support system that employs an algorithm to assess individual risk of becoming long-term unemployed and that informs counselors to assign interventions accordingly. We study the human interaction with algorithms in this context using the lenses of human detachment from and attachment to decision-making. Employing a mixed-method research approach, we show the complexity of enacting the potentials of the data-driven decision-making in the context of a public agency.

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