Jay W. Forrester Professor of Management at the MIT Sloan School of Management and a Professor in the MIT Institute for Data, Systems, and Society.

Director of the MIT System Dynamics Group and the MIT Sloan Sustainability Initiative.

Interactive simulations for climate policymakers and the public 

The reality and risks of climate change stand in stark contrast to widespread confusion, complacency, and denial among policymakers and the public. Few policymakers are trained in science, and public understanding of climate change is poor. But the problem is deeper. Our mental models cause persistent errors and biases in complex systems like the climate and economy. The dynamics of such systems arise from multiple feedbacks, nonlinearities, time delays, and accumulations, but we have difficulty recognizing and understanding these and other attributes of complex adaptive systems.

These problems afflict both laypeople and highly educated elites trained in STEM. They arise not only in complex systems like the climate but also in familiar contexts such as filling a bathtub. Therefore, they cannot be remedied by providing more information, but require different modes of communication, especially experiential learning environments such as interactive simulations. I’ll illustrate with the widely used En-ROADS climate policy simulation and summarize evidence on its effectiveness with the public and senior leaders in government, business, and civil society.

JR East Professor of Engineering

Head, Department of Civil and Environmental Engineering, MIT

Core Faculty, Institute of Data, Systems, and Society, MIT

Principal Investigator, Lab for Information and Decision Sciences

Collective Decision Making and Liquid Democracy 

Joint work with Manon Revel (Meta, formerly IDSS), Daniel Halpern (Harvard), Joe Halpern (Cornell), Adam Berinsky (MIT), and Ariel Proccacia (MIT) 

In this talk,  I will discuss some of my group’s research on collective decision making, including information aggregation, social learning, and a new paradigm for voting called liquid democracy. Specifically,  I will discuss a `new` voting paradigm called liquid democracy, which is based on the dynamics of random transitive delegations on a graph. This paradigm allows voters to choose between directly voting and transitively delegating their votes to other voters, so that those selected cast a vote weighted by the number of delegations they received. In the epistemic setting, where voters decide on a binary issue for which there is a ground truth, previous work showed that a few voters may amass such a large amount of influence that liquid democracy is less likely to identify the ground truth than direct voting. We quantify the amount of permissible concentration of power and examine more realistic delegation models, showing they behave well by ensuring that (with high probability) there is a permissible limit on the maximum number of delegations received. Our theoretical results demonstrate that the delegation process is similar to well-known processes on random graphs that are sufficiently bounded for our purposes. Along the way, we prove new bounds on the size of the largest component in an infinite Polya urn process, which may be of independent interest. In addition, we empirically validate the theoretical results, running six experiments (for a total of N = 168 participants, 62 delegation graphs and over 11k votes collected). We find that empirical delegation behaviors meet the conditions for our positive theoretical guarantees. Overall, our work alleviates concerns raised about liquid democracy and bolsters the case for the applicability of this emerging paradigm.

STL Champion Professor of Urban and Real Estate Sustainability

Faculty Director, MIT Center for Real Estate (CRE),

Faculty Director, MIT Sustainable Urbanization Lab, CRE, DUSP, SA+P

Building Decarbonization Pathway with Technology, Market, and Policy Uncertainties

Decarbonizing the real estate and building sector is critical for achieving net-zero goals in the US and also globally. Despite recent advancements in electrification technologies for commercial and residential buildings, market adoption is hindered by behavioral, policy, and market barriers and uncertainties. We aim to develop a framework to quantify the financial and non-financial costs, benefits, barriers, and incentives of building decarbonization pathway options and the impact of uncertainties.

The framework will be able to support policymakers’ and practitioners’ decision-making by identifying required incentives (all types of subsidies) and regulatory programs, and by providing essential parameters and simulations to accelerate the adoption of decarbonization technologies. This framework can also be applied to a real estate decarbonization case study, using real-world scenarios to provide actionable insights. Such applications can generate practical recommendations for practitioners and governments to enhance the social and business benefits of building decarbonization while mitigating barriers and costs.

President-Elect, International Council on Systems Engineering (INCOSE)

Retired from NASA after 34 years, serving as the MSFC Advanced Concepts Office Technical Advisor responsible for systems analysis of launch vehicles, space transport vehicles, landers, and scientific spacecraft.

Adaptability: A Characteristic of Complex Systems or a Confounding factor of Complexity?

Adaptability is one of 14 characteristics of complex systems.  Complex systems proactively and/or reactively change function, relationships, and behavior to balance changes in environment and application to achieve system goals. Complex systems vary in their application of this characteristic.

As a system’s ability to adapt becomes more responsive, responding to a broader set of conditions, adaptability becomes a confounding factor of complex systems, termed complex adaptive systems (CAS). In addition, the ability to model adaptability becomes more difficult as the system’s ability to adapt moves toward CAS.  The number of modeling types that can support adaptability fall off as the system complexity is confounded indicating the need for research and development in modeling of complex system adaptability.