Recent reviews stated that the complex and context-dependent nature of human decisionmaking resulted in ad-hoc representations of human decision in agent-based land use change models (LUCC ABMs) and that these representations are often not explicitly grounded in theory. However, a systematic survey on the characteristics (e.g. uncertainty, adaptation, learning, interactions and heterogeneities of agents) of the representation of human decision in LUCC ABMs is missing. To inform this debate we performed a quantitative review of 134 LUCC ABM papers using a standardised questionnaire with a particular focus on the characteristics and the theoretical foundation of human decision-making. Thereby, we investigated whether implementations of human decision-making in current LUCC ABMs are theory based. Additionally, we assessed to which degree key factors such as learning, interaction or economic, environmental or social influence factors are considered in human decision making sub-models. We show that most human decision sub-models are not explicitly based on a specific theory and if so they are mostly based on economic theories. In contrast, promising psychological theories such as the theory of planned behaviour are the exception. The key factors of human decision sub-models showed a huge diversity and are not strongly related to neither the characteristics of the specific studied systems (e.g. rural vs. urban or its geographic location) nor the applied theoretical paradigm. We finish by presenting approaches for consolidating and enlarging the theoretical basis for modelling human decision-making.
Agent-based models (ABMs) are increasingly recognized as valuable tools in modelling humanenvironmental systems, but challenges and critics remain. One pressing challenge in the era of "Big Data" and given the flexibility of representation afforded by ABMs, is identifying the appropriate level of complicatedness in model structure for representing and investigating complex real-world systems. In this paper, we differentiate the concepts of complexity (model behaviour) and complicatedness (model structure), and illustrate the non-linear relationship between them. We then systematically evaluate the trade-offs between simple (often theoretical) models and complicated (often empirically-grounded) models. We propose using pattern-oriented modelling, stepwise approaches, and modular design to guide modellers in reaching an appropriate level of model complicatedness. While ABMs should be constructed as simple as possible but as complicated as necessary to address the predefined research questions, we also warn modellers of the pitfalls and risks of building "mid-level" models mixing stylized and empirical components.
A new special collection in JGR: Solid Earth and Earth and Space Science seeks papers from across disciplines that provide insights into induced seismicity at different spatial and temporal scales.
The European research project Innovative Sensor System for Measuring Perceived Air Quality and Brand Specific Odours (SysPAQ) is started under the VIth framework programme under the work programme "New and Emerging Science and Technology" (NEST PATHFINDER "Measuring the Impossible"). The Kick-off of the project was on the first of September 2006. Ten partners (3 Companies, 4 Universities, 3 research Institutes) from 5 countries are involved.The main goal of this project is to develop an innovative system to measure indoor air quality as it is perceived by humans to be used as an indicator and a control device for the indoor air quality. The system will also be able to detect brand specific odours and it will serve as a novel interior odour design tool for the vehicle industry.
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