Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making

Abstract

Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

Will AI Write Scientific Papers in the Future?

“…We have a project called MINT, for Model INTegration, where we are looking at how climate is affecting water availability, agriculture and food production, socioeconomic factors, with extremes of flooding and drought cutting across all those aspects (Gil et al 2021). To study these types of problems, we have to integrate climate models, models for hydrology, agriculture models, economics models, and social models.…”

Perspectives on automated composition of workflows in the life sciences

“…Semantic domain modelling is hard. 67 Especially in highly collaborative community efforts like EDAM/bio.tools, OntoSoft and SADI, it is important to realize that the controlled vocabulary defined by the domain ontology constitutes a kind of social contract that all tool annotators must understand and respect. Using the same interpretations of the terms defined by the ontology is crucial for the meaningfulness and consistency of the domain model.…”

Hydrological Impact of Ilisu Dam on Al-Gharraf River in southern of Iraq using MIKE11 Hydrodynamic Model

“…In order to quantify it in this study, a technical approach might be required [14]. River modelling supports decision-makers to find a suitable way to prevent and predict flood events (flood analysis), drought periods, and the planning and operation of hydraulic structures along rivers [15]. Computer modeling techniques aid engineers by more precisely determining data on river hydrology and hydraulic behavior [16].…”