 | ScenarioWizard |
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The ScenarioWizard software was developed to perform CIB analysis and has been used, tested and further developed in numerous application projects and method experiments. It is provided in two versions: ScenarioWizard for Windows and ScenarioWizard Online. Both versions can be used free of charge. The data formats of both versions are compatible so that the project files can be shared between the two versions.
ScenarioWizard for Windows: |
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ScenarioWizard for Windows is available as a free download (Windows 10 or higher recommended). On Mac OS systems the software can be operated with the help of a Windows virtualization tool (e.g. Oracle 'VirtualBox' or 'Parallels Desktop'). To increase the usability of the software for confidential scenario studies, the program is designed as offline software that does not connect to the Internet after installation. You can find an overview of working with the ScenarioWizard for Windows here. Downloads: → ScenarioWizard 5.32 Manual (pdf/7 MB) → ScenarioWizard 5.32 Software (msi file/4 MB) Recommended citation: Weimer-Jehle, W. (2026). ScenarioWizard: Constructing consistent scenarios using cross-impact balance analysis (Version 5.32) [Software]. ZIRIUS, University of Stuttgart. Available from https://www.cross-impact.org/english/CIB_e_ScW.htm. |
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ScenarioWizard Online: |
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ScenarioWizard Online is a free browser-based alternative to the PC version of the software. As a web application, it can be used by Windows users, Mac users, Linux users and with any type of tablet without installation. With ScenarioWizard Mobile, a special interface is also provided that is optimized for use with smartphones. Links: → Basic video tutorial (21 min) → Login/Create account Recommended citation: Weimer-Jehle, W. (2024-26). ScenarioWizard: Constructing consistent scenarios using cross-impact balance analysis [Web application]. ZIRIUS, University of Stuttgart. Access at https://www.scenariowizard.org. |
Specialized tools
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While the ScenarioWizard was designed by the CIB Lab as a general-purpose software package for CIB analysis, scholars have developed several freely available tools that can perform specific types of CIB analysis with greater depth and variety. A non-exhaustive list is provided below. Inquiries about these tools should be directed to their respective authors. JuCIB (Iain Morrow, 2026) JuCIB is a high-speed CIB-solver developed by Iain Morrow. Through a number of techniques including a branch and bound tree pruning algorithm, it can efficiently solve larger matrices than ScenarioWizard. For matrices that are too time-consuming to evaluate using the ScenarioWizard, it is recommended to use the JuCIB solver. In addition, JuCIB provides transition analysis, and a fast succession algorithm that allows for the exact or approximate calculation of attractor weights (basin sizes) for large matrices more quickly than with ScenarioWizard. It is written in Julia but it includes code to allow it to be used from Python; look in the 'python' folder on GitHub for instructions on how to do this. It also includes a simple user interface. → Download Julia package via GitHub → Windows installer via GitHub (Click on the latest installer build and use the download link provided in the section 'Artefacts') Prophecy (Norman Kearney, 2026) Prophecy is a Python package designed by Norman Kearney. Prophecy provides advanced capabilities for deterministic and stochastic succession analysis, perturbation analysis, and pathway analysis within CIB. It exercises control over succession at the level of individual descriptors (allowing descriptors to change at different rates) and includes functions for complex causation (more than pairwise interactions) and identifying (noisy) cycles. The scenario space can be sampled with balanced maximin designs, which are space-filling and increase the chance of discovering all attractors within large matrices. Perturbation analysis explores how to transform an attractor or reroute a succession. Pathway analysis reports all possible successions between a pair of scenarios. Sensitivity analysis is possible for all analyses (multiple handcrafted matrices or procedurally generated versions). Several descriptive and diagnostic statistics are available. → Download Python package via GitHub → Download Python package via Zenodo |