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

Yao Hu, Christopher J. Quinn, Ximing Cai, Noah W. Garfinkle

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)29-40
Number of pages12
JournalAdvances in Water Resources
StatePublished - Nov 2017


  • Agent-based modeling
  • Behavioral uncertainty
  • Boosted regression trees
  • Bounded rationality
  • Directed information graph
  • Probabilistic graphical model

ASJC Scopus subject areas

  • Water Science and Technology

Fingerprint Dive into the research topics of 'Combining human and machine intelligence to derive agents’ behavioral rules for groundwater irrigation'. Together they form a unique fingerprint.

Cite this