Marco A. Janssen

Informal urban expansion, or conversion of land to urban land uses, outpaces formal urbanization in the developing world. Understanding why this informality exists and persists is essential to counteract characterizations that it is chaotic and ungovernable. This research examines who shapes the informal arrangements developed to meet unmet housing needs that expand the urban footprint, from social housing projects to concentrated squatting in Mexico City metropolis from 2000 to 2016. Institutional analysis elucidates the distribution of payoffs in the “action situation” where decisions about urban land are made, and among “institutional entrepreneurs”, actors that repeatedly evade or alter formal rules or create new rules of urban land regulation. We use interview data regarding the distribution of costs and benefits among 54 actors involved in recent informal urban expansion to provide low- and middle-income housing (2000–2016) to identify potential leverage points for institutional change. We describe four types of informal urban land transactions: i) urbanizing individual plots of land, ii) flipping or subdividing land into multiple parcels, iii) invading land, and iv) manipulating social and public housing developments. We find institutional entrepreneurs—intermediaries, developers, and politicians—disproportionately benefit from and reinforce unplanned urban expansion. These entrepreneurs provide housing for the urban poor, but with social and environmental costs, including exploitation of informal settlers and urbanization of conservation land and loss of environmental services. Disaggregating informality into its component pervasive institutions and analyzing the distribution of payoffs in and beyond Mexico City provides insights about governance for urban sustainability.

The unprecedented use of Earth’s resources by humans, in combination with increasing natural variability in natural processes over the past century, is affecting the evolution of the Earth system. To better understand natural processes and their potential future trajectories requires improved integration with and quantification of human processes. Similarly, to mitigate risk and facilitate socio-economic development requires a better understanding of how the natural system (e.g. climate variability and change, extreme weather events, and processes affecting soil fertility) affects human processes. Our understanding of these interactions and feedback between human and natural systems has been formalized through a variety of modelling approaches. However, a common conceptual framework or set of guidelines to model human–natural-system feedbacks is lacking. The presented research lays out a conceptual framework that includes representing model coupling configuration in combination with the frequency of interaction and coordination of communication between coupled models. Four different approaches used to couple representations of the human and natural system are presented in relation to this framework, which vary in the processes represented and in the scale of their application. From the development and experience associated with the four models of coupled human–natural systems, the following eight lessons were identified that if taken into account by future coupled human–natural-systems model developments may increase their success: (1) leverage the power of sensitivity analysis with models, (2) remember modelling is an iterative process, (3) create a common language, (4) make code open-access, (5) ensure consistency, (6) reconcile spatio-temporal mismatch, (7) construct homogeneous units, and (8) incorporating feedback increases non-linearity and variability. Following a discussion of feedbacks, a way forward to expedite model coupling and increase the longevity and interoperability of models is given, which suggests the use of a wrapper container software, a standardized applications programming interface (API), the incorporation of standard names, the mitigation of sunk costs by creating interfaces to multiple coupling frameworks, and the adoption of reproducible workflow environments to wire the pieces together.

The use of agent-based models (ABMs) for investigating land-use science questions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning—Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation, field and laboratory experiments, companion modeling, and GIS and remotely sensed data. This paper reviews these five approaches to informing ABMs, provides a corresponding case study describing the model usage of these approaches, the types of data each approach produces, the types of questions those data can answer, and an evaluation of the strengths and weaknesses of those data for use in an ABM.

Keywords: agent-based model, empirical parameterization, human–environment interactions, household surveys, experiments, companion modeling, participant observation, spatial data

Many government and private programs provide incentives for non-industrial private forest (NIPF) owners. Due to the complexity of this web of programs, the incentives of the programs are unclear. We focus on four specific programs that represent different rule structures—a federal cost-share program, a state tax incentive program, a nationwide private stewardship program, and a local private conservation organization. We perform institutional analysis of the formal and informal rules of the programs based on literature review, discussions with officers, and formal guidelines of the programs. We classify different types of rule structures, and explain them in relation to goals and organizational structures of the programs.

Keywords: Forest; Government programs; Non-governmental organizations; Institutions

This article presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decision making, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.

Keywords: agent-based modeling, cellular automata, complexity theory, land-use and land-cover change, multi-agent systems

The use of agent-based modeling (ABM) has recently been extended to the study of natural resource management and land-use and land-cover change. Many ABM applications have been at a conceptual and abstract level, which helps scholars to recognize how macro patterns can emerge from simple rules followed by agents at a micro-level. ABM has a greater potential than many other approaches to capturing the dynamic relationships between social and ecological systems. This paper contributes to a larger effort to explore how individual decision making by a heterogeneous set of landowners, given local biophysical conditions, led to the particular aggregate pattern of land-cover change in Indiana, with an emphasis on forest-cover change. In our preliminary effort, we created a model structure that allowed examination of the institutional impact of government programs on individual land-use decisions. Our model is based on the concept that an initial condition endows an agent with a particular set of beliefs and desires that could lead to any number of intentions, actions, and outcomes. Institutions have the potential to intervene in an agent’s decision-making process and alter their beliefs and desires by providing information and incentives. The next crucial step in our effort will be to extend this model to study the impact of other political institutions, such as taxation and zoning, as well as utilize the conceptual model to facilitate the implementation of institutions in the agent-based model.