KTAB Applications:

Compliance with Sanctions on Iran
This study evaluates the expected international compliance of US-led snapback sanctions targeting Iran’s export of crude oil and condensates, during and after the issuance of a limited set of waivers to some Iranian customers. Importers of Iranian crude and condensates will need to consider the political and economic ramifications in the context of an evolving geopolitical picture. Over time, changing geopolitical conditions may result in shifting resolve to maintain initially-agreed sanctions, and perhaps to extend, adjust, or terminate the waivers. We will assess the changing extent of political will to comply with this second wave of sanctions, and the expected order of magnitude of compliance in terms of barrels per day.

Political Will for Restarting Nuclear Power in Japan
A study that evaluates the political will of stakeholders in Japan, at a municipal, prefecture, and state level, to restart nuclear reactors. We assess the dynamics of political support and opposition to this question, as well as the timing and potential scope of nuclear power coming back online. Additionally, we assess the displacement of LNG as a result of nuclear power restarting so that the impact on global markets can be evaluated.

Political Feasibility of Nationally Determined Contributions under the Paris Agreement
This project evaluates the political feasibility for signatories of the Paris Agreement under the United Nations Framework Convention on Climate Change (UNFCCC) to effectively implement and iteratively improve their Nationally Determined Contributions (NDCs) such that the rise of global temperature is limited to 2℃ above pre-industrial levels (or less). This includes five “deep dive” studies for each of the top 5 emitters. This includes China, the US, the European Union, India, and Russia. Additionally, we focus on a global, geopolitical perspective for this question based on non-expert data.


KTAB Technical & Software Developments:

CDMPs, Neural Networks, and Machine Learning
This project integrates the logic of a KTAB-style model of collective decision-making processes (CDMPs) with machine learning to develop a hybrid approach that can be used to anticipate politically-driven outcomes for population-scale analytic questions. If successful, this will extend the applicability of KTAB to include the decision-making of families, households, and actors at the scale of a market or country’s population, as opposed to only finite, bounded, and limited numbers of actors. This CDMP Deep Learning methodology could then be used to provide a bottom-up perspective on politically-feasible choices for an entire economy. We are currently using TensorFlow to develop a deep-learning neural network that predicts household vehicle purchasing decisions in an entire market. The source data includes behavioral and demographic characteristics for households drawn from the California Vehicle Choice dataset. The neural network includes an additional layer of calculations that implements a KTAB-style CDMP.

KTAB Deep Learning Neural Network Application on GitHub https://github.com/kapsarc/KTAB-DeepLearn

Hybrid Political-Economic Modeling
This project integrates models of Collective Decision-Making Processes in KTAB with economic models, so as to create hybrid models that endogenously develop and bargain over economic policies. This research includes formal development of hybrid models as well policy-relevant applications to evaluate the utility and effectiveness of the integrated approach. Currently, we are developing a hybrid KTAB-style CDMP model integrated with a dynamic stochastic general equilibrium model, applied to Saudi Arabia. Unlike most other KTAB research, this CDMP uses an Enumerated Model of Politics (EMP).

iKTAB
iKTAB is a simplified, web-based version of KTAB that does not require installation of any software on a local PC. iKTAB is currently in beta release, and can be accessed here.

Advanced KTAB Visualization
The team is engaged in extending the quality and variety of visualizations in KTAB. We are particularly interested in the application of network diagram techniques for between-turn dynamics, multidimensional problems, and sensitivity analysis (with respect to both actor attributes and model parameters).