Natural and engineered physical, cyber, and social infrastructure systems provide a critical foundation and have shaped generations of civilization across the globe. They are the fabric that connects the spatial, social, and economic structure of cities and nations, and provide critical services for healthy living, economic well-being, and the security of human communities. Recently, our interdependent social, technical, and economic systems have seen much stress due to population growth, climate change, and social inequities. Natural resources and environmental quality have also become increasingly stressed, posing tremendous challenges that individuals, communities, and nations must confront. As systems become more complex, they become increasingly difficult to design and manage. Uncertain and potentially emergent behavior arises from interdependencies in the system that existing methods and traditional design processes fail to capture effectively, if at all. Our ability to innovate the design and management of complex systems is confronting theoretical and institutional barriers.

Can we imagine a better world that can meet the needs of twice today’s population with half today’s resources while providing better liveability for all? This is a Grand Challenge. Many diverse disciplines—including engineering, economics, sociology, public policy, and psychology—are required to address aspects of the challenge and capture elements of the opportunity. We also must integrate knowledge across the relevant disciplines in a way that directly supports public policy design and analysis based on an understanding of individual stakeholder decisions and behaviors, and interactions among stakeholders.

Global Aspiration Oasis (GAO) aims to turn the Grand Challenge into an enabler rather than inhibitor of long-term sustainable development. It supports the rapidly expanding international research, education, and application community seeking to understand and manage the interactions between infrastructure, the population it serves, the environment in which it functions, technology, and the economy. It will create a shared, facilitated research, education, and application environment in which social scientists, engineers, industrialists, policy makers, and other stakeholders can research, learn, and practice together to understand how better to exploit the technical, institutional, financial, and market opportunities that emerge from the increased interdependence of social-technical systems. GAO will focus on the development and implementation of innovative business models and aims to support industries wishing to exploit them in markets. As a consequence, GAO has a major focus on using its activities to catalyze broader debates and initiatives that contribute toward a more sustainable, economically vibrant, and fair society. GAO will undertake a wide range of research, education, and application activities. Examples include, but are not limited to:

• GAO Systematics

• GAO Systems Innovation

• GAO Systems Enterprise

• GAO Systems Leadership and Management

• GAO Education

• GAO Consortium

The Systems Process

Systems Thinking is the fundamental tool of the Systems scientist or engineer. It is a problem-solving methodology based on holistic, systematic thinking, and characterized by the separation of problem formulation from solution concepts, of solution-neutral intent from solution-specific function, and of function from form.

Systems Modeling and Analysis emphasizes the integration of models from different disciplines and operating at different time and spatial scales, and the analysis of the performance, cost/schedule, and risk aspects of the system during its entire lifecycle, from cradle (conceptual development) to grave (disposal).

Systems Architecting and Design is the creative process of synthesizing a system to satisfy a selected set of stakeholder needs. The process follows an iterative path from needs to goals to solution-neutral, then solution-specific functions, to components that can perform those functions. It also provides a framework to conduct holistic trade studies between different architecture alternatives.

Enterprise and Society teaches Systems thinkers to go from promising system designs to successful products, services, and companies that have a real, broad, and positive impact on society.

The Scientific Pillars

Cognitive Sciences are needed to develop human models that are grounded in what we know about the human brain and cognition, particularly as it relates to decision-making. We know, for example, that human decision-making is influenced by complex evaluations of risk, reward, and temporality (to name a few), and associated neural substrates. Further, individual and cohort (e.g. older, younger) differences in these factors must be incorporated into decision models.

Decision Sciences are used to provide a theoretical basis for the decision-making processes in humans and intelligent agents. For example, consumers’ decisions can be modeled using microeconomic principles, such as utility maximizing behavior, as well as behavioral extensions that represent choices that depart from maximizing utility.

Network Sciences are the foundations on which we model the interaction of the different agents and components of the CHPS. Over the years, for example, the idea of congestion pricing has been developed applying market mechanisms to reduce the social costs associated with traffic congestion. Because the additional cost of a new user entering a network (external marginal cost suffered by other users following a new user’s entrance) is not internalized, road markets generally fail. Network science is needed to formulate fundamental extensions of the standard optimal road usage analysis by including externalities other than congestion, by considering the simultaneous maximization of the resulting social welfare for both inside and outside the congestion charge area.

Data Science is the knowledge required to fuse the sources of information rigorously and efficiently available to the humans and intelligent agents, and transform them into actionable knowledge and decisions. For example, active and passive data (cell phone traces) provide samples of the whereabouts and movements of individuals and are sources of information for models of daily activities in a city.