Table 7 Characteristics of agent-based and system dynamics models, and a hybrid model (Martin and Schlüter 2015)

Characteristic question

How do emergent system-level interaction (e.g. spatially, between individuals?)

How do stocks change or stabilize? (given that rates are constant) Which process/feedback is dominating?

How do changing process rates (impacted by decisions) affect dynamics?

How do changing stocks affect agent states/the distribution of traits?

Purposes in general for all: improve system understanding rather than prediction or forecasting

To identify mechanisms (specific interactions) that are responsible for emerging system-level patterns (disaggregated)

Generate hypotheses, exploration of micro-level behavior.

Investigate system-level dynamics (aggregated), stability properties of the system, loop dominance, explaining temporal dynamics, projection into the future.

Investigating different micro- or system-level mechanisms that drive certain dynamics. Generate hypotheses of system state-change (when does dominance of feedbacks change?) or structural development over time (when does an average trait of agents change?)

Focus

Micro-level interactions between entities, network structure (heterogeneous characteristics of individuals/actors, temporal discrete behavior), transient dynamics.

Processes driving accumulation in stocks at (sub-)system level, stable-states, feedbacks (balancing, amplifying), non-linearities.

Process of restructuring in a system which can focus either on a structure affecting the processes, or processes affecting the structure.

Tests for model calibration

Statistical pattern matching-can the model grow patterns that are found in reality?

Stability analysis-under which parameter setting can fixed points/equilibria occur? How stable are they?

Separate sub-system test (paradigm specific) and qualitative check for the coupled version.

Suitable and traditional analysis tools, typical experiments

Only through simulations, often with multiple repetitions because of stochastic elements: plotting group/system-level characteristics over time (average), evaluation a limited parameter range, describing transient dynamics.

Simple models through analytical tools (basins of attraction, bifurcation analysis, overall stability), and more complex through simulations (state space plots from simulations, evaluating stable-states, equilibria.)

Through simulations with a focus on either

1. change in structure/ parameters: how does it affect the dynamics?

2. Change in dynamics: how does it affect the structure?

Type of outcome

Emerging spatial/agent patterns, scenario comparison between structurally different model versions, system properties such as the average state of a population.

Aggregated system properties in terms of stability, loop dominance.

Time series of merging state-transitions.