AI News, Artificial Intelligence for Computational Sustainability: A Lab Companion/Introduction

Artificial Intelligence for Computational Sustainability: A Lab Companion/Introduction

motivates and crystalizes computational sustainability thusly: 'it is imperative that computer scientists, information scientists, and experts in operations research, applied mathematics, statistics, and related fields pool their talents and knowledge to help find efficient and effective ways of managing and allocating natural resources.

To that end, we must develop critical mass in a new field, computational sustainability, to develop new computational models, methods, and tools to help balance environmental, economic, and societal needs for a sustainable future.'(pp.

For example, designing and setting aside a protected chunk of land for grizzly bears may seem straightforward at first glance, perhaps requiring attention to the monetary costs of various plots of land and the protection agency's available budget, but this problem quickly becomes more complex as decision makers factor in lost opportunity costs by ranchers, residential developers, and other stakeholders who might, at a later date, fight to retract this protection and recoup opportunities;

Designing effective solutions doesn't simply require that practitioners understand computing principles (e.g., abstraction), methods (e.g., optimization) and models (e.g., of differential equations) and that they be generally adroit at computational thinking so as to advance the state of the computational art;

but computational sustainability also requires that practitioners understand those aspects of the sustainability challenges to which computation will be applied -- in reserve design this can include attention to economics (opportunity costs), human behavior (stakeholders), animal behavior, ecological dependency webs, and climatic change.

It is not surprising that in the nascent field of computational sustainability, few individuals have all the requisite knowledge for even small problems, and that effective practitioners are typically (members of) highly interdisciplinary research teams, of which computational scientists are a vital part.

Importantly, the scope of computational sustainability goes beyond direct 'managing and allocating natural resources,' to include the development and management of human-made resources such as synthetic materials and energy production, all of which use the natural world as a source and a sink for these activities and their residue.

Even computing itself -- the manufacture, use, and disposal of computing artifacts -- have an increasingly large ecological footprint, and computational methods for developing environmental and society-friendly technology through holistic, cradle-to-cradle design methodologies are a subject for computational sustainability.

Rather, the most realistic promise of AI for promoting sustainability thinking is as a partner in human decision making, with AI tools and agents designed to meet people where people are, 'fit' to human limitations, but not confined by these limitations -- indeed, AI designed so that the hybrid human/AI decision maker goes well beyond the capabilities of the human alone or the AI alone.

The purpose of this lab text is to support student exploration of sustainability-motivated AI systems, with the vast majority currently falling into the cognitive prosthesis paradigm, with expectations that numbers in the collaborative agent paradigm will increase, and with any useful distinction between the paradigms eventually disappearing.

Though computational sustainability arguably crystalized around 2008 and 2009[1], with activity in the area taking off coincident with dedicated conferences and workshops, research into computational models and methods to address problems of environmental and societal sustainability has been going on for much longer.

The fascinating history of climate modeling illustrates this long pairing nicely: 'To be sure, the computer at Phillips's disposal was as primitive as the dishpan (its RAM held all of five kilobytes of memory and its magnetic drum storage unit held ten).

in fact, Axelrod's supposition that for cooperation to arise, the future must cast a sufficient 'shadow' on the present is an insight that can be realized through mathematics and computation (e.g., policy learning, virtual worlds, visualization) to mitigate the myopia and egocentrism of decision making.

Following a number of early pioneers in computational sustainability research and a swell of support in policy, corporate, and military circles for 'green' ICT, the founding of an institute, conferences and conference tracks in 2008-2009 appear to have been seminal in growing computational sustainability.

In the conceptual framework of 1st, 2nd, 3rd order effects of the OECD and others, most of the research opportunities will relate to the higher order effects of AI, for example, the 2nd order effects of intelligent planning/routing/scheduling software that reduces travel time and idling in a smart street system, all the way to the highest order effects of changing the way that humans, in conjunction with smart tools, think -- in ways that are evidence-based, strategic, long-term, and for the collective good.

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