Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

Authored by Ximing Cai, Yao Hu, Christopher J Quinn, Noah W Garfinkle

Date Published: 2017

DOI: 10.1016/j.advwatres.2017.08.009

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a ``gray box{''} approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm-corn price, underlying groundwater level, monthly mean temperature and precipitation-have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems. (C) 2017 Elsevier Ltd. All rights reserved.

Tags

Agent-based modeling graphs models Bounded rationality networks Decision-Making Socioecological systems Rainfall Precipitation Hadoop Behavioral uncertainty Probabilistic graphical model Directed information graph Boosted regression trees Directed information Water cycle