Prophesy: A new tool for dynamic CIB

Dynamic system models are simplified representations, used to understand how a set of interacting elements (a system) might change over time. When observational and experimental data about system interactions are lacking, but an understanding of the system is still desired, judgments are sometimes used. While judgments may be wrong, organizing them into a model and analyzing their dynamics can help to support more rigorous decision making. Cross-Impact Balances (CIB) is a method for modelling systems using multiple data types, including judgments (Weimer-Jehle 2006). For many years, CIB has been used almost exclusively to assess system elements for their internal consistency. Combinations of system elements (scenarios) that are internally consistent represent the system’s attractors, i.e., the system states towards which the system is inclined. Attractors comprise an important but limited part of a system’s overall “stability landscape”, the n-dimensional space that represents all the system’s possible states. When the current state of a system is not internally consistent (transient state), the system will move along the stability landscape towards one or another attractor. The pathways that a system takes along the stability landscape are also worth studying for a variety of reasons. A system in a transient state could pass by several attractors, some of which may represent situations that stakeholders want to avoid. Small perturbations along the system’s path could knock the system into an undesirable attractor. Stakeholders may also want to avoid certain transient states. Knowledge of the stability landscape can help stakeholders to plot paths around undesirable transient states and towards desirable attractors. Stakeholders may also want to understand how a system might react to efforts to guide or transform it (e.g., to identify the most robust solutions and avoid unintended consequences), or how the system might react to outside shocks (e.g., to shore up desirable situations).CIB has the potential to contribute to understanding in each of these areas, and in this talk, I will demonstrate a new tool for realizing this potential: Prophesy. Prophesy is written in Visual Basic for Applications and distributed as an Excel add-in. It includes standard CIB features, such as the global and local succession rules, while incorporating stochastic succession (Schweizer et al. 2013) and introducing several new features. For a given matrix, the user can analyze all scenarios, a random sample (Monte Carlo), or a custom batch. The user can specify the number of steps per succession, the chance per step of a random event, and the number of succession trials per scenario. After finding the International Congress on Environmental Modelling & Software iEMSs attractors (or using a custom batch of scenarios), the user can test how the specified scenarios respond to perturbations (Kearney 2021). The results are displayed in a “transformation matrix”, where the rows and columns list the scenarios that were analyzed, and the cells report the perturbations that resulted in a transformation from one scenario to another. Prophesy also provides support for exploratory modelling (Lempert 2019) and Boolean logic. Future versions will add additional features, such as cycle detection for stochastic succession; support for fuzzy logic; and dynamic impacts.As an illustration of Prophesy, I will present a stylized model of social-ecological interactions, which elaborates the famous IPAT equation (environmental impacts = population * affluence * technology). The model synthesizes insights from a variety of literatures, including evolutionary sociology, ecological and evolutionary economics, social and environmental psychology, political science, and world systems theory. I use the model to test strategies for activating, accelerating, and steering societal transitions towards sustainability, while also assessing how progress could be stalled or reversed. Since the model is judgment-based, I explore multiple versions of the model, each with a different set of variable coefficients and/or changes to the model’s equations. The solutions are then ranked according to their robustness against the uncertainties.Kearney, Norman M. 2021. “Guided Cultural Evolution and Sustainable Development: Proof of Concept and Exploratory Results.” https://doi.org/10.13140/RG.2.2.31015.88488.Lempert, R. J. 2019. “Robust Decision Making (RDM).” In Decision Making under Deep Uncertainty, edited by Vincent A. W. J. Marchau, Warren E. Walker, Pieter J. T. M. Bloemen, and Steven W. Popper, 23–51. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-05252-2_2.Schweizer, Vanessa J., Alastair Jamieson-lane, Nix Barnett, Hua Cai, Stephan Lehrer, Matteo Smerlak, and Melinda Varga. 2013. “Complexity (Trans--)Science: A Project on Forecasting Social Change.” Santa Fe Institute Summer School.Weimer-Jehle, Wolfgang. 2006. “Cross- Impact Balances: A System-Theoretical Approach to Cross-Impact Analysis.” Technological Forecasting and Social Change 73 (4): 334–61. https://doi.org/10.1016/j.techfore.2005.06.005.