  Social-ecological systems research aims to understand the nature of
social-ecological phenomena, to find ways to foster or manage conditions under
which desired phenomena occur or to reduce the negative consequences of
undesirable phenomena. Such challenges are often addressed using dynamical
systems models (DSM) or agent-based models (ABM). Here we develop an iterative
procedure for combining DSM and ABM to leverage their strengths and gain
insights that surpass insights obtained by each approach separately. The
procedure uses results of an ABM as inputs for a DSM development. In the
following steps, results of the DSM analyses guide future analysis of the ABM
and vice versa. This dialogue, more than having a tight connection between the
models, enables pushing the research frontier, expanding the set of research
questions and insights. We illustrate our method with the example of poverty
traps and innovation in agricultural systems, but our conclusions are general
and can be applied to other DSM-ABM combinations.
