This section provides key information about the SOSIEL Harvest Extension. The following section provides examples of its applications.
What is the SOSIEL Harvest Extension (SHE)?
The SOSIEL (Self-Organizing Social & Inductive Evolutionary Learning) Harvest Extension (SHE) implements boundedly-rational decision-making by one or more agents. Each SOSIEL agent makes decisions using a cognitive architecture that consists of nine cognitive processes that enable each agent to interact with other agents, learn from its own experience and that of others, and make decisions about taking, and then take, (potentially collective) actions. Together, LANDIS-II with SHE has the potential to simulate adaptive management in coevolving coupled human and forest landscapes.
Each agent can engage in anticipatory learning, goal prioritizing, counterfactual thinking, innovating, social learning, goal selecting, satisficing, signaling, and (potentially collective) action-taking.
Four alternative cognitive levels, with each level composed of a different combination of cognitive processes and representing an alternative approach to modeling agent behavior.
Two alternative modes: Mode 1, which is primarily intended for simulating site-scale forest management, and Mode 2, which is intended for simulating stand-to-landscape-scale forest management.
Latest official release: Version 1.1.12 — February 2021
Full release details found in the SHE User Guide and on GitHub.
To use SHE, you need:
The LANDIS-II model v7.0 installed on your computer.
Input files (see examples below)
Version 1.1.12 can be downloaded here. To install it on your computer, just launch the installer.
Sotnik, G., Cassell, B. A., Duveneck, M. J., Scheller, R. M. (Submitted) A new agent-based model provides insight into assumptions in modeling forest management under deep uncertainty.
LANDIS-II requires a global parameter file for your scenario, and then different parameter files for each extension. Example files are here.
If you have a question, please contact Garry Sotnik at firstname.lastname@example.org. You can also ask for help in the LANDIS-II users group.
If you come across any issue or suspected bug when using SHE, please post about it in the issue section of the GitHub repository.
Garry Sotnik, Brooke A. Cassell, and Robert M. Scheller
A new agent-based model provides insight into assumptions in modeling forest management under deep uncertainty
Abstract: Exploratory modeling in forestry uses a variety of approaches to study forest management questions. One key assumption that every approach makes is about the degree of deep uncertainty—the lack of knowledge required for making an informed decision—that future forest managers will face. This assumption can strongly influence simulation results and the conclusions drawn from them, but is rarely studied. Our objective was to measure the degree of deep uncertainty within a forest management simulation to compare alternative modeling approaches and improve understanding of when a specific approach should be applied. We first developed a method for measuring the degree of deep uncertainty assumed by approaches to modeling forest management. Next, we developed a new extension to the LANDIS-II model, the SOSIEL Harvest Extension, which simulates alternative approaches to modeling forest management. Finally, we applied the new method and extension to comparing three alternative approaches to modeling forest management in Michigan. The degrees of deep uncertainty varied substantially among the three modeling approaches. There is also an overall negative relationship between the degree of deep uncertainty an approach assumes a forest manager will face and the level of flexibility the approach assumes a manager will have in responding to forest change. Quantifying the deep uncertainty inherent in simulated forest management and comparing it across models provides an opportunity to better understand its sources and investigate differences in the assumptions made by alternative modeling approaches.
Keywords: agent-based model, comparative analysis, deep uncertainty, forest management.