HOW I WORK / FROM MECHANISMS TO UNDERSTANDING

Small interactions.
Bigger understanding.

I help people explore how individual behaviors and environmental conditions shape complex systems.

Your question comes first: what do you need to understand, compare, or decide? From there, we define the individuals, behaviors, interactions, and environmental processes that matter, then build an agent-based model to explore how they combine to produce system-level outcomes.

01 / HOW PATTERNS EMERGEFrom individual behavior to the bigger picture

  1. Individuals

    + behavioral rules
    + environment

  2. Interactions

    Respond, move, and adapt
    as conditions change

  3. Emergent patterns

    Collective outcomes
    across the system

System-level patterns arise from the interactions of individuals and their environment.

MODEL THE INTERACTIONS

Explore how
patterns emerge.

Agent-based modeling represents individuals, such as fish, with their own conditions and behavioral rules. Each responds to other individuals and its environment. As these interactions unfold, broader patterns emerge: where animals gather, how they move, or how exposure varies across a population.

For example, a fish’s movement and feeding influence which contaminants it encounters. Simulating those processes helps us examine how behavior and changing environmental conditions combine to shape exposure. We can compare scenarios, test possible explanations, and investigate which assumptions influence the results.

I work with you to connect those experiments to your original question, drawing on observations and domain expertise to assess the results. The aim is a tool your team can understand, use to explore alternatives, and revisit as new evidence becomes available.

MAKE THE REASONING VISIBLE

Understand
the assumptions.

A useful model should be open to examination. I make its rules, supporting evidence, assumptions, and tests visible so collaborators can follow how an explanation becomes a simulation. That makes it easier to question choices, interpret unexpected behavior, and understand what the model can tell us.

DESIGN FOR WHAT COMES NEXT

Build knowledge
that carries forward.

These priorities of transparency and reuse led me to modular simulation design and frameworks. I organize mechanisms into independently documented and tested components, preserving the scientific reasoning alongside the code. Domain libraries give related components a shared structure, allowing suitable mechanisms to be compared, adapted, and combined for new questions.

Reuse still calls for judgment about the species, system, and scale. Testing individual components is a foundation; the assembled model must also be evaluated for its intended application.

02 / HOW KNOWLEDGE CARRIES FORWARDPreserve the mechanism. Extend its use.

  1. Documented mechanisms

    Rules, evidence,
    assumptions, and tests

  2. Shared library

    Common structure
    and explicit connections

  3. Models for different questions

    Select, adapt, combine,
    and evaluate in context

RESEARCH IN PRACTICE

A foundation
to build on.

GoFish puts this approach into practice: a modular library of fish behaviors and physiological processes for investigating migration and contaminant exposure in estuaries. It provides a foundation for carrying ecological knowledge across appropriate applications.

This work also informs Agentarium, an AI-assisted platform in development for creating traceable draft agent-based models that researchers can review and refine.

Explore GoFish Meet Agentarium
Let’s explore your system