The emergence of coupled natural and human landscapes marked a transformative interval in the human past that set our species on the road to the urbanized, industrial world in which we live, and enabled technologies and social institutions responsible for human-natural couplings in domains beyond rural, agricultural settings. The Mediterranean Landscape Dynamics Project (MedLand) is studying the interacting social and biophysical processes associated with these novel socioecological systems and their long-term consequences using a new form of 'experimental socioecology' made possible by recent advances in computation. We briefly describe the MedLand modeling laboratory, a hybrid simulation environment that couples models of smallholder farming and herding, landscape evolution, and vegetation change managed through an interaction model. We then review three examples of experimental socioecology carried out in this laboratory. These offer new insights for scale-dependent thresholds in agropastoral productivity, long-term sustainability of alternative land-use strategies, and identifying signatures of human and climate-driven landscape dynamics. We conclude with an overview of new directions for this interdisciplinary research on Anthropocene human-earth systems, including: modeling more diverse decision-making strategies for land-use, developing more sophisticated models of vegetation dynamics and fire ecology, and generating digital proxy data for more robust model validation against the empirical record.

Cellular automata (CA) provide a convenient approach to modeling a system comprised of homogeneous entities that, generally, have a spatial relationship with one another. CA are used to model systems that can be appropriately represented as a collection of interconnected automata. These networked automata may act as either a model representation of the entire system, or used to model a sub-system within a hybrid system. As the sub-systems within a hybrid system are disparate, so too can the models representing them be disparate using a multi-model approach. However, to take advantage of multi-modeling, CA and other models used to represent the sub-systems must be founded on system-theoretical principles. Furthermore, each model’s formalism must account for input and output data exchange with other modeling formalisms. Therefore, to support modular synthesis of distinct CA models with non-CA models, a composable cellular automata (CCA) formalism is proposed. This formalism is provided as a domain-neutral, mathematical specification. The CCA is then exemplified as part of a multi-model, and the GRASS development environment is used to describe one possible implementation approach.

It is becoming commonplace to use multiple types of models together for simulating multifaceted systems across many scientific disciplines. Indeed, in recent years, some approaches (referred to as multimodeling or multiformalism modeling) have been developed for representing a complex system as a set of subsystem models. Among these, there has been an interest in developing hybrid methods where structures and behaviors of models are explicitly accounted for. Furthermore, theories and approaches are proposed to define the interactions among heterogeneous model types. However, modeling a system this way brings about composition complexity that must also be managed. The complexities of hybrid modeling resulting from the interactions of the composed models can be reduced using interaction models, an approach referred to as polyformalism modeling (Sarjoughian 2006). Independently developing and utilizing such interaction models provides additional flexibility in system model design, modification, and execution for both the subsystem models and the resultant hybrid system model.

Existing Cellular Automata formalisms do not consider heterogenous composition of models. Simulations that are grounded in a suitable modeling formalism offer unique benefits as compared with those that are developed using an adhoc combination of modeling concepts and implementation techniques. The emerging and extensive use of CA in simulating complex heterogeneous network systems heightens the importance of formal model specification. An extended discrete-time CA modeling formalism is developed in the context of hybrid modeling with support for external interactions.

Modeling and simulation is pervasive throughout many different disciplines. As computing technology has provided more capability, the systems being modeled and simulated have grown larger and more complex. Often times, these large systems are managed as interacting subsystems. When it is necessary for the simulation to allow disparate subsystems to maintain their independence, then a hybrid model of the subsystems should be used. Furthermore, to ease the burden of verification and validation of simulation results, a proven system theoretical modeling specification should be used. However, many communities have already adopted nonsystem theoretical software solutions and established a group of domain experts familiar with these tools. This paper provides two things: a formal approach to building a hybrid model, and a discussion of how to incorporate a nonsystem theoretical software implementation into a proven framework. The first is done through the implementation of a Knowledge Interchange Broker (KIB) as an Interaction Model (IM). The second is accomplished by exemplifying the use of the IM in an agent-environment hybrid model. In the hybrid model, the agent is implemented in the Discrete-event System (DEVS) specification and the environment is implemented in the Geographical Resources Analysis Support System (GRASS) using a Composable Cellular Automaton (CCA) specification. This concept has been successfully applied to both example models and an interdisciplinary research project where the interactions between human activities and landscape processes are studied.