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The 'ODD' (Overview, Design concepts, and Details) protocol was published in 2006 to standardize the published descriptions of individual-based and agent-based models (ABMs). The primary objectives of ODD are to make model descriptions more understandable and complete, thereby making ABMs less subject to criticism for being irreproducible. We have systematically evaluated existing uses of the ODD protocol and identified, as expected, parts of ODD needing improvement and clarification. Accordingly, we revise the definition of ODD to clarify aspects of the original version and thereby facilitate future standardization of ABM descriptions. We discuss frequently raised critiques in ODD but also two emerging, and unanticipated, benefits: ODD improves the rigorous formulation of models and helps make the theoretical foundations of large models more visible. Although the protocol was designed for ABMs, it can help with documenting any large, complex model, alleviating some general objections against such models.

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call patternoriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity. What makes James Bond an agent? He has a clear goal, he is autonomous in his decisions about achieving the goal, and he adapts these decisions to his rapidly changing situation. We are surrounded by such autonomous, adaptive agents: cells of the immune system, plants, citizens, stock market investors, businesses, etc. The agent-based complex systems (1) (ACSs) around us are made up of myriad interacting agents. One of the most important challenges confronting modern science is to understand and predict such systems. Bottom-up simulation modeling is one tool for doing so: We compile relevant information about entities at a lower level of the system (in Bagent-based models,[ these are individual agents), formulate theories about their behavior, implement these theories in a computer simulation, and observe the emergence of system-level properties related to particular questions (2, 3).Bottom-up models have been developed for many types of ACSs (4), but the identification of general principles underlying the organization of ACSs has been hampered by the lack of an explicit strategy for coping with the two main challenges of bottom-up modeling: complexity and uncertainty (5, 6). Consequently, model structure often is chosen ad hoc, and the focus is often on how to represent agents without sufficient emphasis on analyzing and validating the applicability of models to real problems (5, 7).A strategy called pattern-oriented modeling (POM) attempts to make bottom-up modeling more rigorous and comprehensive (6,(8)(9)(10). In POM, we explicitly follow the basic research program of science: the explanation of observed patterns (11). Patterns are defining characteristics of a system and often, therefore, indicators of essential underlying processes and structures. Patterns contain information on the internal organization of a system, but in a Bcoded[ form. The purpose of POM is to Bdecode[ this information (10).The motivation for POM is that, for complex systems, a single pattern observed at a specific scale and hierarchical level is not sufficient to reduce uncertainty in model structure and parameters. This has long been known in science. For example, Chargaff_s rule of DNA base pairing was not sufficient to decode the structure of DNA-until combined with patterns from x-ray...

Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.

Five software platforms for scientific agent-based models (ABMs) were reviewed by implementing example models in each. NetLogo is the highest-level platform, providing a simple yet powerful programming language, built-in graphical interfaces, and comprehensive documentation. It is designed primarily for ABMs of mobile individuals with local interactions in a grid space, but not necessarily clumsy for others. NetLogo is highly recommended, even for prototyping complex models. MASON, Repast, and Swarm are “framework and library” platforms, providing a conceptual framework for organizing and designing ABMs and corresponding software libraries. MASON is least mature and designed with execution speed a high priority. The Objective-C version of Swarm is the most mature library platform and is stable and well organized. Objective-C seems more natural than Java for ABMs but weak error-handling and the lack of developer tools are drawbacks. Java Swarm allows Swarm’s Objective-C libraries to be called from Java; it does not seem to combine the advantages of the two languages well. Repast provides Swarm-like functions in a Java library and is a good choice for many, but parts of its organization and design could be improved. A rough comparison of execution speed found MASON and Repast usually fastest (MASON 1-35% faster than Repast), Swarm (including Objective-C) fastest for simple models but slowest for complex ones, and NetLogo intermediate. Recommendations include completing the documentation (for all platforms except NetLogo), strengthening conceptual frameworks, providing better tools for statistical output and automating simulation experiments, simplifying common tasks, and researching technologies for understanding how simulation results arise.

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A 0.9 km-reach of Uvas Creek, California, was reconstructed as a sinuous, meandering channel in November 1995. In February 1996, this new channel washed out. We reviewed project documents to determine the basis for the project design and conducted our own historical geomorphological study to understand the processes operating in the catchment and project reach. The project was designed using a popular stream classification system, based on which the designers assumed that a "C4" channel (a meandering gravel-bed channel) would be stable at the site. Our historical geomorphological analysis showed that the reach had been braided historically, typical of streams draining the Franciscan Formation in the California Coast Ranges, with episodic flows and high sand and gravel transport. After the project washed out, Uvas Creek reestablished an irregular, braided sand-and-gravel channel, although the channel here was narrower than it had been historically, probably due to such factors as incision caused by gravel mining. Our study casts doubt on several assumptions common in many stream restoration projects: that channel stability is always an appropriate goal; that channel forms are determined by flows with return periods of about 1.5 years; that a channel classification system is an easy, appropriate basis for channel design; and that a new channel form can be imposed without addressing the processes that determine channel form.