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The Virtual Special Issue "Agents for Theory" discusses theory development using agent-based models (ABMs). The six contributions focus on how the word "theory" is used in the ABM literature, how reviews of ABMs should be conducted to gain general insights, how even the conceptualisation of ABMs can help to transform heuristic theories into scientific ones, how the context-dependent choice of decision models can be better justified, how reusable building blocks (RBBs) of ABMs could support theory development, and how a modular framework of RBBs can be used to identify general solutions. Overall, theories of decision making that go into ABMs and theories of system dynamics that come out of ABMs are interrelated, so the micro-macro perspective and the attempt to identify and reproduce patterns at both levels simultaneously is a way forward. Theory development requires clearer communication using a common language, reference to patterns, and more detailed model analysis and testing. • We provide a detailed summary of the Special Issue on theory development with agent-based modelling. • The Special Issue condenses discussions between experts from a wide range of disciplines. • Clearer communication of context and a stronger focus on reproducing multiple patterns are suggested as a way forward. • We outline a vision of how the virtual laboratory provided by ABMs may be leveraged to foster theory development. [ABSTRACT FROM AUTHOR]

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. • Reusable Building Blocks (RBBs) are effective in software development but are underrepresented in agent-based modelling. • RBBs describe individual mechanisms or processes, distinguishing them from modules that represent entire subsystems. • We present a first website that demonstrates how a RBB template and repository can be organized. • Examples of RBBs are provided to illustrate the concept, fostering discussion and encouraging further refinement. [ABSTRACT FROM AUTHOR]

The description of complex river environments requires interdisciplinary approaches to collect and manage manifold data types and sources. Deriving comprehensive knowledge from complex data sources is challenging and necessitates not only knowledge of environmental science but also statistics and Software engineering. This study introduces a relational database framed in an application called River Analyst for creating and managing river data with open-source standards (Python3 and Django). We conceptualize data models of river environments, which describe sediment characteristics and hydraulics related to hyporheic exchange. River Analyst enabled us to derive novel insights for restoring rivers affected by so-called riverbed clogging, notably, fine sediment infiltration in the hyporheic zone. The database analysis reveals that clogging is not a dominant control process when the fraction of fine sediment exceeds 50%–55%. In conclusion, the new Software holds promise for data-informed advancements in augmenting knowledge to restore ecologically functional hydro-environments. • A database app in Django enables scalable, centralized management of fluvial data. • Database scheme enhances data analysis through linked parametrical data. • Open-source framework can leverage data-driven decisions for river restoration. [ABSTRACT FROM AUTHOR]