Multi-platform competence center

We inspire economic and social transformation by producing, collecting and disseminating frontier research and science-backed evidence, with a view of helping enterprises and society develop practices, strategies and policies consistent with rising to the “grand challenges”.

Automatic Detection of Leadership from Voice and Body

Whether facing dire and urgent crises at national level or leading an organization toward success, political and organizational leadership shapes our society. For instance, leadership might determine how a country can successfully face a great health issue (e.g., COVID19 pandemic).

Evidence-Base Environmental Policy Advice

The purpose of this platform is to contribute to the debate in politics and media in Switzerland and abroad regarding environmental policy-making. It does so by providing a timely overview of the best economic research addressing the issue being debated, in terms that non-specialists can understand.

Grand Challenges and the Role of Business Firms

Grand challenges represent global societal challenges of ecological or social nature, such as the transition to a carbon-free economy, fighting global inequality, and tackling precarious working conditions that are emerging as a result of digitalization and robotization. These challenges are complex and thus require the coordinated and sustained effort from several public and private actors, including business firms.

Redesigning the pharmaceutical R&D landscape for the post-COVID-19 economy

Assessing the distribution of research efforts among small companies, large companies and universities can provide important - and urgent - insights into the design of policies to encourage and support pharmaceutical innovation to combat COVID-19 and other diseases.

Shaping the future of mobility

Urgent environmental challenges as well as the sharing economy have spurred the recent emergence of new business models related to shared mobility. The associated innovative transportation paradigms require a reduction of vehicle fleets as well as an optimization of travel times in order to pave the way towards sustainable mobility. Yet, sustainable mobility sharing solutions do not only require considering the environmental impact, but also have to be viable for firms and their customers. Only meeting the needs of all stakeholders allows for a large-scale implementation of such disruptive transportation concepts and hence yields a significant impact on the future of our society.

Shaping the future of work

New developments in technology – artificial intelligence, Internet-of-Things (IoT), robotics and virtual assistants – are challenging the current world of work. Workers may see their job “taken away” by automation- focused technology. But technological advances also offer huge potentials to do work differently and do different work. Changing technological setups at the workplace require organizations and workers to often and quickly embrace new tools, new forms of interaction and new forms of work. These changes will also likely have a fundamental impact on our society and social fabric.

Socially Inclusive Technologies for Shared Prosperity

How can disruptive technologies be made socially non‐disruptive ? The question is important because it pertains to the contribution of basic research to human development and shared prosperity, both in the most advanced economies and in the Global South. The question is complex because it calls for close collaboration between natural scientists, engineers and social scientists.

Systemic Risks and Sustainability

In an increasingly interconnected world, our planet as well as human community are facing systemic events that occur very infrequently but whose impacts may be catastrophic on society at large. We can mention climate change and financial crises as typical examples.

Toward Interpretable Machine Learning

Many of today’s decision-making processes are increasingly being made by computer-based systems: from whether a company receives a loan, to whether an individual’s parole is granted. However, modern machine learning techniques, including deep learning, focus solely on predictive accuracy and completely disregard the topic of interpretability. This shortcoming has significantly curtailed the adoption of state-of-art machine learning techniques in many industries, including healthcare, finance, insurance and law, in which regulations and business practice require transparent, trustful and auditable decision support systems.