Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 86
3
Programmatic and Institutional
Opportunities to Enhance Computer
Science Research for Sustainability
The challenges of achieving a sustainable society are truly global,
with complex interdependencies that affect risk assessments, technical
and social opportunities for solutions, and economic and political feasi-
bility. No one field or discipline on its own could possibly be expected to
“solve” even one aspect of this problem. However, information is central
to making progress on many fronts. Thus computer science (CS)—which
couples information and innovation—is vital to sustainability. For com-
puter science to play its part in meeting global sustainability challenges,
priority should be given to research that addresses one or more important
sustainability challenges (examples were described in Chapter 1) and that
offers significant impact. This impact may be direct, or it may be through
game-changing contributions that offer significant leveraging opportuni-
ties for other domains. In either case, priority should be placed on oppor-
tunities to address the sustainability challenge to a tangible degree.
This chapter explores some of the potential impediments within the
field of computer science to making significant progress on issues per-
tinent to sustainability. It considers how to bridge the gap between the
traditional research quest for universality1 and the imperative to have
a specific impact on sustainability challenges. The chapter is aimed pri-
1The committee uses the term “universality” to encompass the related notions of general-
izability (solutions that are amenable to relatively straightforward abstractions in order to
address more general versions of a given problem) and breadth (solutions that can be revised
to be applicable to broad problem domains and spaces). The quest for universality captures
the traditional CS research goals of abstractability and broad applicability.
86
OCR for page 87
PROGRAMMATIC AND INSTITUTIONAL OPPORTUNITIES 87
marily at the CS research community—including both researchers and
funders. First, it discusses some of the fundamental aspects of CS research
and computational thinking and how these aspects are also critical to the
sustainability problem space. Then it explores the challenge of universal-
ity and emphasizes that a bottom-up approach is not only necessary in
the sustainability space but also has precedent in many other areas of
deep computer science. It describes the connection between universality,
bottom-up approaches, and sustainability. It then offers suggestions on
how to structure research to promote meaningful impact on sustainability.
Finally, the chapter identifies methodological opportunities for optimiz-
ing research outcomes and impacts.
COMPUTER SCIENCE APPROACHES
FOR ADDRESSING SUSTAINABILITY
Chapter 2 highlighted the centrality of data and information to sus-
tainability. Given this centrality, computer science and information tech-
nology (IT) are essential to meeting sustainability challenges. The chal-
lenge for IT experts and CS researchers is in ensuring that technologies
and approaches represent usable, appropriate solutions; that they are
highly effective; and that they take advantage of the deepest and most
powerful insights that can be brought to bear. IT has been and continues
to be a critical enabler of progress in vast arenas of society. Sustainability
is no exception: IT offers a powerful tool to assist in addressing sustain-
ability challenges.
Moreover, fundamentals of the computer science field itself offer
unique and important contributions to sustainability. To name just a few
such fundamentals, consider abstraction design, algorithms, operating
systems and layering, real-time systems, machine learning, human com-
puter interaction (HCI), and databases. For instance, the very notion of
queryable structured data is at the heart of much of computer science; at
the same time strides are being made to cope with the vast amounts of
unstructured data now available. Given the scope and scale of sustainabil-
ity challenges along with the vast amounts of relevant data, the structur-
ing and understanding of these data present many challenges. The lens of
computational thinking is essential to solving many complex problems, 2
and there are key opportunities within computer science that are clearly
2See National Research Council, Report of a Workshop on the Scope and Nature of Compu-
tational Thinking, Washington, D.C.: The National Academies Press (2010); and National
Research Council, Report of a Workshop on the Pedagogical Aspects of Computational Thinking,
Washington, D.C.: The National Academies Press (2011).
OCR for page 88
88 COMPUTING RESEARCH FOR SUSTAINABILITY
BOX 3.1
Additional Areas of Promising Computer Science
and Related Research for Sustainability
In addition to the research areas discussed in Chapter 2 of this report, fol-
lowing is a list presenting a sampling of topics and areas that arise in computer
science and information technology more generally that are likely opportunities for
making progress in sustainability.
• Science of resilience and adaptive systems. This is a likely area of opportu-
nity especially as it applies to self-regulating processes, biodiversity, and metrics
of adaptability.
• Design for robustness, resilience, graceful degradation, and the decoupling
of abstractions from implementation (for instance, designing for average-use cases
and building in techniques for degradation, as opposed to designing for peaks with
safety margin).
• Mass customization, especially in the role of programming languages. This
is a likely area of opportunity for many levels of programming, at many stages of
the life cycle.
• Understanding technology in context. One cannot understand how technol-
ogy will affect sustainability without understanding what people will do with it. The
emphasis in computer science on extensibility in system design takes into account
the fact that technology as used matters, not just as designed.
• Design thinking. This area is involved with the invention of things that people
will use and engage with, which is crosscutting for multiple domains.
• Search—a profound advance resulting from decades of research and in-
novation in multiple areas. What is the equivalent of search in the physical world?
How do we deal with unstructured search, taxonomy, structured query process-
ing—search for data relevant to scientific discovery?
• Computer vision. This field offers likely opportunity as a modality for search-
ing and understanding the physical world.
• Representation for purposes of discovery. This is an area of opportunity in
terms of representation of the physical world and of sustainability problems.
• Social media. This area of opportunity relates to information support and
sharing, building community, structured argumentation, sensing, modeling, and
observation.
• Tools for the automated design of very large scale systems. This is an area
of opportunity that includes the development of capabilities to cope with challenges
where there is functional decomposition
applicable, even beyond those highlighted in Chapter 2. A sampling of
these areas is outlined in Box 3.1.
As one example, many sustainability challenges, particularly those
related to infrastructure, make salient the importance of architecture.
Architecture encompasses not just structural connections among subsys-
tems, but expectations regarding what a system will do, how its perfor-
OCR for page 89
PROGRAMMATIC AND INSTITUTIONAL OPPORTUNITIES 89
mance will scale, what behaviors are within bounds, and how subsystems
(or external actors) should interact with the system as a whole. This type
of challenge can be seen perhaps most clearly in the smart grid example
discussed in Chapter 1. However, the other illustrative examples in Chap-
ter 1—food systems and the development of sustainable and resilient
infrastructure—also make clear that early architectural choices (such as
the expected role and behavior of individual farms in the global food
system, or the anticipated communications capabilities of first responders
in a crisis) can have long-lasting repercussions.
A system’s architecture instantiates early design decisions and has
a significant effect on the uses, behaviors, and effects of the system long
past the time when those decisions were made. Moreover, requirements
inevitably change over time, necessitating flexible or evolvable designs.
Because of this large effect of a system’s architecture on almost all aspects
of the system over its life cycle, the architecture of larger-scale systems
of necessity merits significant attention and resources. As systems have
become global in scale, the disciplines of computer science and software
engineering have grappled with the challenges of architecture as they
pertain to large-scale systems working over large geographic areas, with
countless inputs and millions of users. Lessons from architecting hard-
ware, software, network, and information systems thus have broader
applicability to the processes of structuring, designing, maintaining,
updating, and evolving of infrastructure in pursuit of sustainability. 3
One question not yet addressed in this report is how well the CS
research community is poised to play its part in meeting global sustain-
3For an in-depth examination of the importance of architecture in software-intensive sys-
tems, see Chapter 3 of the following report: National Research Council, Critical Code: Software
Producibility for Defense, Washington, D.C.: The National Academies Press (2010). It describes
the importance of architecture as follows (pp. 68-69): “Architecture represents the earliest
and often most important design decisions—those that are the hardest to change and the
most critical to get right. Architecture makes it possible to structure require ents based on
m
an understanding of what is actually possible from an engineering standpoint—and what
is infeasible in the present state of technology. It provides a mechanism for communications
among the stakeholders, including the infrastructure providers, and managers of other sys-
tems with require ents for interoperation. It is also the first design artifact that addresses
m
the so-called non-functional attributes, such as performance, modifiability, reliability, and
security that in turn drive the ultimate quality and capability of the system. Architecture
is an important enabler of reuse and the key to system evolution, enabling management
of future uncertainty. In this regard, architecture is the primary determiner of modularity
and thus the nature and degree to which multiple design decisions can be decoupled from
each other. Thus, when there are areas of likely or potential change, whether it be in system
functionality, performance, infrastructure, or other areas, architecture decisions can be made
to encapsulate them and so increase the extent to which the overall engineering activity is
insulated from the uncertainties associated with these localized changes.”
OCR for page 90
90 COMPUTING RESEARCH FOR SUSTAINABILITY
ability challenges. In the committee’s view, one perceived barrier to its
doing so is the sense that aiming research toward sustainability challenges
may conflict with ultimate scientific aims of universality.4 This question is
explored below in more detail.
The most powerful and important computer science innovations to
date share the characteristic of universality. Indeed, it is their utility across
a wide range of domains that makes their aggregate impact so great. See
Box 3.2 for a short list of just some of computer science’s most signifi-
cant achievements. Universities, research laboratories, departments, and
funding agencies increasingly recognize the value of multidisciplinary
research. However, as the field has matured, there has been comparatively
less emphasis on domain-driven approaches to innovation in favor of
research that attempts to go directly to universality—that is, abstractabil-
ity and breadth. Nevertheless, it is a strength of computer science that
the field can, and does, ground its advances in real-world problems. As
described in Chapters 1 and 2, CS can contribute significantly and criti-
cally to sustainability. In this section it is argued that to have the biggest
impact on the pressing challenges facing the world today, CS research
must be informed with deep knowledge, input, and context from domain
experts.
In some areas of computer science, universality is built into the prob-
lem definition. Much of theoretical computer science, of course, begins
by representing the target problem in abstract, symbolic language. Other
examples of research with universality as the focus from the start include
the von Neumann computer model itself, programming languages such
as Fortran and ALGOL, and early human-factors research (such as that
of Doug Englebart and Alan Kay) that created new modes of human-
computer interaction. In other equally consequential areas, however,
broad applicability has only emerged years or even decades later, as
researchers began with domain-specific problems and developed solu-
tions and then later were able to generalize and understand deeper truths
from this panoply of specific contributions. Examples of important con-
tributions that began as highly specific projects include the World Wide
Web (originally conceived as a means to share research papers and scien-
tific information) and object-oriented programming (early object-oriented
languages were developed to address specific problems such as discrete
event simulation or graphical interaction).
In this chapter it is argued that CS research on sustainability is best
approached from the bottom up: that is, by developing well-structured
4The integration of computer science with domain sciences was a central tenet of a 1992
National Research Council report: National Research Council, Computing the Future: A
Broader Agenda for Computer Science and Engineering, Juris Hartmanis and Herbert Lin (eds.),
Washington, D.C.: National Academy Press (1992).
OCR for page 91
PROGRAMMATIC AND INSTITUTIONAL OPPORTUNITIES 91
BOX 3.2
Universality and Computer Science’s Greatest Achievements
The list below offers one view of some of computer science’s most significant
achievements over the years. “Computer science” is construed broadly here, to
encompass information and communications technologies.1 Most of the achieve-
ments listed below were accomplished by focusing on developing domain-specific
solutions with an eye toward eventual abstraction and universality.
• Universality—the Turing machine
• Computability and hardness—P, NP, and complexity classes
• Stored program computer—execute anything
• Operating system (versus operator)
• The Internet and Internet Protocol—move anything anywhere
• File storage—move anything forward in time
• Coding, decoding, and cryptography
• Programming language and its translation
• Layered design (versus vertical integration)—abstraction as a foundation
• Microprocessor, the personal computer
• Audio/video representation
• Distributed systems
• Self-describing schemas, documents
• Computer-aided design, computer-aided manufacturing, computer-aided optimiza
tion, computer-aided engineering (design, simulate, build, test, measure, use)
• Database query languages and management systems
• Continuous user input (pointers, clickers)
• Graphical user interfaces in various forms
• Parallel execution
• Numerical simulation of physical phenomena
• The web, hypertext, and distributed markup in a global namespace
• Massive keyword search
• Spreadsheets
• Scheduling, planning, optimization
• V
ery-large-scale integration, design rules, synthesis, verification, massive sys-
tems production
• Search techniques, heuristics
• Machine learning
• Structure of graphs
1There are, of course, other takes on this question. The Computer Science and Telecom-
munications Board’s (CSTB’s) well-known “tire tracks” diagram (as seen in National Research
Council, Innovation in Information Technology, Washington, D.C.: The National Academies
Press [2003]) explores innovations in computer science and information technology from the
perspective of spawning billion-dollar industries. That list includes time-sharing, client/server
computing, entertainment, Internet, local area networks, workstations, graphical user inter-
faces, very-large-scale integration design, and reduced instruction set computing processors.
A CSTB report published in 2004 articulated the essential character of computer science, and
focused on seven key themes: computer science (1) involves symbols and their manipulation,
(2) involves the creation and manipulation of abstractions, (3) creates and studies algorithms,
(4) creates artificial constructs, (5) exploits and addresses exponential growth, (6) seeks the
fundamental limits on what can be computed, and (7) focuses on the complex, analytic, rational
action that is associated with human intelligence. (National Research Council, Computer Sci-
ence: Reflections on the Field, Reflections from the Field, Washington, D.C.: The National Acad-
emies Press [2004].) The differences in these lists are less important than noting the power of
focused problem solving in a field whose core strengths include abstraction and adaptability.
OCR for page 92
92 COMPUTING RESEARCH FOR SUSTAINABILITY
solutions to particular, critical problems in sustainability and later seek-
ing to generalize these solutions, as opposed to striving for universality
from the start. Many advances will require CS research for progress, as
described earlier, but those advances may not be immediately evident as
universal approaches. The committee believes that demanding evidence
of clear universality from the start is likely to inhibit close interdisciplin-
ary collaboration and, ultimately, major advances. Moreover, many sus-
tainability challenges need to be addressed sooner rather than later, even
if imperfectly. At the same time, the fact that the problems associated
with sustainability are complex, multifaceted, and in some cases poorly
defined means that close attention to experimental robustness and under-
lying mathematical rigor will be essential.
Previous chapters illustrate this overall approach. For example, the
hypothesized research on the smart grid takes an approach to the problem
that is fundamentally a computer networks perspective (inspired by the
success of the Internet), but it is not initially intended to make universal
contributions to the theory of networks. Rather, by focusing on the prob-
lem at hand—that of integrated control of power generation, distribution,
and use—there is the potential for breakthrough advances on this critical
issue at the same time that new computational techniques are being devel-
oped. There are clearly general lessons to be learned about these issues.
The committee suggests that the best way to learn them is to start with
the particular sustainability challenge at hand, make progress on that, and
later seek to generalize.
This approach does not mean, however, that any application of com-
putation or IT to problems in sustainability should automatically be seen
as computer science research for sustainability. Rather, to be judged as a
significant contribution within the intersection of CS research and sus-
tainability, the contribution first must have the potential to make a real
difference in moving toward a more sustainable future. Second, the contri-
bution must have the potential, if it is successful, to add to generalizable
knowledge about sustainability, and the contribution or proposed solu-
tion should, at the same time, require new computational techniques or
thinking beyond the current state of the art in computing.
The specific criteria for judging research success should of course
evolve over time, with members of the community themselves proposing
and debating what constitutes the most worthy research. The committee
emphasizes, however, the criterion of having the potential to make a real
difference. An open research question in its own right is how best to assess
and evaluate impacts and how to isolate the effects of any particular sets
of interventions, given the scales and time frames of many sustainability
challenges.
OCR for page 93
PROGRAMMATIC AND INSTITUTIONAL OPPORTUNITIES 93
PRINCIPLE: Encourage research at and across disciplinary boundar-
ies, well informed by specifics and well structured to handle scale,
data, integration, architecture, simulation, optimization, iteration,
and human and systems aspects. CS research in sustainability should
be an interdisciplinary effort, with experts in the various fields of
sustainability being equal partners in the research.
TOWARD UNIVERSALITY
Although the committee emphasizes that a premature focus on uni-
versality would be detrimental to the kind of high-impact sustainabil-
ity solutions so desperately needed, universality should not be ignored.
Indeed, domain-specific research can lead toward universality. A chal-
lenge, though, is how to pursue the universality that contributes so much
to the power of computer science. In this section, it is argued that the
purposes of domain specificity and contextualization are not at odds with
ultimately producing universality in results, and that universality is not
achieved directly in most cases in any event. Consider the development
of important advances in CS and IT. Achieving universality typically
involves developing well-structured innovative solutions, applying them
to the problem at hand, evaluating their efficacy, and using this evalua-
tion to guide further improvement, enhancement, and new directions.
Successful approaches are then refined and applied in other areas, per-
haps similar to the original problem domain, perhaps more remote. As
the iterations of application proceed, the universality of the approach is
discovered and refined.
Why has this approach worked so well in computer science? Despite
the fact that computer science has information at its heart, tools and meth-
ods are ultimately instantiated in software. Software is malleable and well
disposed to iteration. Software technology is developed, deployed, used,
and modified in continuous iterative cycles. Developing modern software
is not done through implementing a perfect software system once, at the
start. Instead, the state of the art in software engineering urges iteration
and architectural flexibility. Software is designed to be updated on a fre-
quent basis over its entire life cycle.
This approach to the creation of software systems has developed for
many reasons. For one reason, the work required to discover all or even
most of the bugs before release in non-critical systems far exceeds the
value of that approach. Similarly, feature sets are expanded through use.
The range and number of possible features of any particular target system
are larger than what is implemented—if that were not the case, the systems
would be even more complex and difficult to use and would take even
longer to roll out. Thus systems are rolled out with a modest feature set,
OCR for page 94
94 COMPUTING RESEARCH FOR SUSTAINABILITY
and new features are then added over time. This approach allows prod-
ucts to get to market sooner and helps to avoid or reduce the development
of features that are not really needed, as well as revealing what actually
works in practice. In short, after initial deployment, reality (instead of
anticipated or modeled reality) guides the evolution of the feature set.
The understanding of the human systems into which all computing sys-
tems are deployed is highly limited (not least because the understanding
and modeling of humans and organizations are highly incomplete and
flawed at best). Thus one cannot generally anticipate and simulate all
uses. The world of computer systems has grown to evolve features that
are adapted to what the users of those systems demand. A virtuous cycle
has resulted—users have come to expect flexibility and malleability, thus
ensuring that feedback loops occur. Systems have versions that are rolled
out as available and as features are demanded. Change management is
a basic fabric of these systems, which are designed for ongoing change.
These systems have innovation and expectations of innovation literally
encoded within them.
Another way to think about the inherent adaptability of computer sci-
ence is to consider an “end to end” argument for the inclusion of authentic
applications in systems research. The original end-to-end argument put
forward by Saltzer, Reed, and Clark was as follows:
[F]unctions placed at low levels of a system may be redundant or of little
value when compared with the cost of providing them at that low level.
. . . The argument appeals to application requirements, and provides a
rationale for moving function upward in a layered system, closer to the
application that uses the function.5
This same logic has implications for systems research and innovation.
Authentic applications should be included as part of systems research
exploration at as high a level as possible in order to keep functional and
performance requirements on a purposeful track. For the purposes of this
report, notions of authenticity must encompass high-impact applicability
to sustainability challenges.
These fundamentals in computer science are relevant to the way
that CS research is done and the way in which research and develop-
ment investment is approached. This “built for change” characteristic
also facilitates the transition from one application to another. Algorithms
and their instantiations can be adaptive, iteratively modified to fit a new
5J.H. Saltzer, D.P. Reed, and D.D. Clark, “End-to-End Arguments in System Design,” Pro-
ceedings of the Second International Conference on Distributed Computing Systems, Paris, France,
April 8-10, 1981.
OCR for page 95
PROGRAMMATIC AND INSTITUTIONAL OPPORTUNITIES 95
context. More importantly, such iterations are not a significant departure
from what occurred in their initial creation; the expectation of iteration
is part of the core of the technology. Building for change is done through
modularity, through system designs resulting from hard thinking about
where to place functionality, through the isolation of errors and details.
Change to software happens as the software is developed, and as it is
deployed, debugged, and iteratively improved; and it happens as it is
applied to a new problem. As a given technique is applied to a new
problem, and yet another new problem, and so on, the universality of
the technique emerges. For each new application, the characteristics of
generality are exposed, and the possibility for further abstraction and
broad applicability grows. In the best case, an “exponential” process
emerges in which techniques that are broadly applicable are exposed as
each successful reapplication enables multiple new adaptations to come
to light. Not all potential new applications are developed, of course, but
those that are find their ultimate universality through bottom-up cycles
of change and through the iterative process of design which promotes
that process. Past successful examples of this approach include language
translation, Internet protocols, machine learning, object-oriented lan-
guages, and databases.
The approaches discussed in Chapter 2 were not described in their
most general terms. The committee does not suggest that they be pursued
generally. But universality is often seen as the ultimate win of computer
science techniques. Although universality is important and must be the
goal because some big wins are needed in order to attack the unprec-
edented challenges of sustainability, the challenges should be approached
through the concrete. There are opportunities for CS research to take on
the key challenges in sustainability, learn about them, and design focused
solutions that work. The design of those solutions should embed the best
of CS design and systems learnings—modularity, isolation, simplicity,
and so on. Then CS researchers and practitioners should experiment with,
apply, and pilot solutions to specific problems; look for the successes and
reapply and adapt them to other applications; and develop universal-
ity while seeking to increase applicability and impact. If the concrete is
embraced across the range of infrastructure, ecosystems, and human sys-
tems, reality will help hone and filter possible approaches, and multiple
and adapted applications will emerge.
FINDING: Fast-moving iterative, incrementally evolving ap roaches
p
to problem solving in computer science, which were critical to build-
ing the Internet and web search engines, will be useful in solving
sustainability challenges.
OCR for page 96
96 COMPUTING RESEARCH FOR SUSTAINABILITY
EDUCATION AND PROGRAMMATICS
The vision described above implies a broadening of what it means
to be a computer scientist. A significant opportunity for change is in the
area of education. This change should include educating computer science
students to achieve impact with computing, computational methods, and
systems approaches in important domain-specific areas. Such a shift in
culture would encourage these students to develop domain expertise and
to collaborate directly with domain experts while in graduate school or
in preparing for graduate work6 and to address such topics as modeling
and predicting energy use and designing for reuse.
Making such a shift successfully will also require a culture of experi-
mentation and innovation in the application of computer science. Further,
it will require a research infrastructure in order to make progress. That
infrastructure should include the following: (1) available standard data
sets, models, and challenge problems to the community in order to assist
in developing a common discourse and target for innovation, analogous
to Grand Challenges in robotics, speech, vision, and so on; and (2) the
building of shared infrastructure through open architectures and test-
beds that allow for grounded iterative experimentation in the context
of real components, both human and technical. Such architectures could
go a long way to increasing the feasibility and impact of experimental
research in academia and to creating an ecosystem that supports iterative
innovation.
Education and training within the target domains constitute an
equally important goal. One challenge is in the translation of problems
from one domain or field to another—for instance, describing the power
and electric grid systems as a dynamical system and control problem—
and then translating sometimes newly exposed assumptions back to the
problem domain. Information and data are critical to understanding the
challenges, formulating solutions, deploying solutions, communicating
results, and facilitating learning and new behaviors that are based on
results of the work. Thus a significant component of meeting virtually all
sustainability challenges is to infuse computational thinking and com-
puter science- and information-rich approaches into the deploying indus-
try and the research and mission agencies.
PRINCIPLE: Undergraduate and graduate education in computer
science should provide experience in working across disciplinary
boundaries. Graduate training grants and postdoctoral fellowships
should support training in multiple disciplines. Undergraduate and
6These shifts are already underway in various fields—for example, biocomputing.
OCR for page 97
PROGRAMMATIC AND INSTITUTIONAL OPPORTUNITIES 97
graduate programs should include tracks that offer introductory
and intermediate course work in such sustainability areas as life-
cycle analysis, agriculture, ecology, natural resource management,
economics, and urban planning.
Research institutions—both universities and the funding organiza-
tions—could better address the needs of authentic multidisciplinary
research, in terms of publication venues, funding, criteria for promotion,
research infrastructures that help enable sustained collaboration, and
cross-training. The latter include the cross-training of students in multiple
fields to enable them to bring a computer science perspective into other
arenas. Authentic multidisciplinary work is challenging.7 Work will need
to be done across disciplinary boundaries and incorporating experts from
many disciplines, as well as individuals with deep expertise themselves
in more than one discipline. Examples of opportunities to enhance multi-
disciplinary approaches are described below:
• The creation of certificate programs, extension programs, and
online programs for professionals in the target industries and agencies
through professional societies and lifelong learning and training;
• Scholarships and fellowships both for computer science graduate
students and for early-career professors that provide financial support
for taking the time to develop expertise in a complementary discipline;
• The development of cross-agency initiatives (such as the collabo-
ration of the National Science Foundation [NSF] with the Environmental
Protection Agency8) that encourage interdisciplinary collaboration in rel-
evant fields;
• Support for the development of new, cross-discipline structures
(perhaps departments or institutes) between computer science and other
fields that can create a new generation of students who are agile both in
computer science and in fields relevant to sustainability;
7For a discussion of some of the challenges, see Sean Eddy’s essay on “antedisciplinary” sci-
ence (Public Library of Science, Computational Biology 1(1), doi: 10.1371/journal.pcbi.0010006),
in which he notes: “Focusing on interdisciplinary teams instead of interdisciplinary people
reinforces standard disciplinary boundaries rather than breaking them down. An interdis-
ciplinary team is a committee in which members identify themselves as an expert in some-
thing else besides the actual scientific problem at hand, and abdicate responsibility for the
majority of the work because it’s not their field.”
8An example is the joint National Science Foundation/Environmental Protection Agency
establishment of two centers to study the environmental implications of nanotechnol-
ogy, described in a 2008 press release: see http://www.nsf.gov/news/news_summ.
jsp?cntn_id=112234.
OCR for page 98
98 COMPUTING RESEARCH FOR SUSTAINABILITY
• Institutional structures that support multidisciplinary and interdis-
ciplinary teams focused on a problem or set of problems over an appro-
priately long period of time;9
• Internships and career paths and placement programs that encour-
age computer science students and postdoctoral researchers to work in
relevant government agencies, non-governmental organizations, and
industries;
• Coordination between academic research in computer science and
non-traditional industrial partners—that is, beyond the large IT compa-
nies—to scope problems, help train students, and cross-fertilize ideas; and
• Regular, high-level summits involving computer science and
sustainability experts—practitioners and researchers—to inform shared
research design, assess progress, and identify gaps and opportunities.
The conceptualization of the bottom-up emergence of universality is
relevant to researchers, university systems, and funding agencies in the
following respects:
• Researchers—emphasizing a bottom-up approach affects how research-
ers select and approach their problems and how they approach the training of
their students. Cross-training, learning other languages and vocabularies,
immersion, and intensive and sustained collaboration are all important
aspects of how research will need to be done. Although it may be essential
to making real progress with respect to sustainability challenges, long-
term commitment to a specific domain area is typically inherently risky
for a CS researcher, because specific problems in sustainability may be
addressed successfully but universal ideas or techniques may not neces-
sarily materialize.
• University systems—a focus on bottom-up approaches affects how uni-
versities incentivize and create the infrastructure for faculty to pursue sustained
multidisciplinary efforts. The computer science community has made prog-
ress in tenure and in the promotion of individuals who straddle disciplin-
ary boundaries. Is such boundary crossing sufficiently encouraged and
explicitly incentivized? Publication rates and pressures are higher than
ever. Publication is essential to a successful R&D ecosystem, but does an
emphasis on frequent publication have a negative effect on the pursuit
of R&D which tackles difficult application domains that have not previ-
ously been processed and translated into computer science problems?
9One successful example of such an effort was the collaboration between computer sci-
entist James Gray and astrophysicist Alexander Shalay on the multiyear effort (which, of
course, involved many others) to develop the Sloan Digital Sky Survey (http://www.sdss.
org/).
OCR for page 99
PROGRAMMATIC AND INSTITUTIONAL OPPORTUNITIES 99
Moreover, much truly multidisciplinary work will require large teams
and collaborations; is there appropriate recognition of CS contributions
to large, multiauthor publications? Also, an evaluation of the productiv-
ity of junior faculty may need to extend to evaluating the impact of the
researcher in the realm of sustainability in addition to the field of com-
puter science. Related to promotion is the question of appointments—
in what departments are multidisciplinary researchers appointed, and
how can such appointments be handled so that the multidisciplinary
nature of the researchers’ work does not count against them in their home
departments?
• Funding agencies—emphasizing bottom-up approaches may affect how
agencies structure multidisciplinary programs. The National Science Foun-
dation is a primary funder of research in computer science in the United
States. The former Information Technology Research Programs at NSF and
its current Cyber-enabled Discovery and Innovation Program have dem-
onstrated the feasibility of programs with significant multidisciplinary
aspects and the impacts that can result. But such programs provide for
a minority of CS research, and in the committee’s view, the sense of the
community, as seen in review panels, program structures, review criteria,
and so on, is not generally favorable toward funding domain-specific
projects. One challenge is that typical reviewers of prospective research
in computer science tend to want to see universality from the start, which
presents a fundamental problem for the CS research community; funding
agencies such as NSF can do little about this matter if the community
does not adapt.
The committee is encouraged by the establishment of Science, Engi-
neering, and Education for Sustainability (SEES) as an NSF-wide area of
investment. SEES aims for a systems-based approach to “promote the
research and education needed to address the challenges of creating a
sustainable human future” and places an emphasis on interdisciplinary
efforts. With its emphasis on interdisciplinarity and the involvement of
NSF’s Directorate for Computer and Information Science and Engineer-
ing, the SEES program offers an opportunity to demonstrate the depth of
IT and CS innovation that the core discipline can offer and the rich and
globally important problem space of sustainability.
There are ongoing opportunities for NSF to take advantage of the sig-
nificant domain expertise in other agencies in order to pursue a strategy
broader than programs that are crosscutting with other research director-
ates within NSF. Such a broadened strategy would involve programs that
connect researchers with domain experts, practitioners, and projects in
relevant mission agencies (such as the Department of Energy, Department
of Transportation, Department of the Interior, and Department of Health
OCR for page 100
100 COMPUTING RESEARCH FOR SUSTAINABILITY
and Human Services).10 Furthermore, it is critical to ensure that funding
structures support data sharing, encourage citability, and so on, but at the
same time, an emphasis on sharing should be balanced with the need for
researchers actually to collect data and to begin solving problems.
Another role that funding agencies can play is to fund longer-term
projects and to be tolerant of risk, particularly in these multidisciplinary
and cross-disciplinary, potentially high-impact research areas. It may be
useful to target special funds that encourage the switching of focus to
sustainability challenges and to incentivize grounded, domain-specific
collaboration and training.11
PRINCIPLE: Refine funding and programmatic options to reinforce
and provide incentives for the necessary boundary crossing and
integration in CS research to address sustainability challenges. In
particular, funding, promotion, and review and assessment (peer
review) models should emphasize in-depth integration with data
and deployments from the constituent domains.
PRINCIPLE: There should be strong incentives at all stages of
research for focusing on solving real problems whose solution can
make a substantial contribution to sustainability challenges, along
with in-depth metrics and evaluative criteria to assess progress.
Another critical issue for structuring research is to build in evaluation
tools for prioritizing efforts and evaluating meaningful impact. The com-
mittee offers an evaluative framework below.
EVALUATION, VIABILITY, AND IMPACT ANALYSIS
One of the greatest challenges in multidisciplinary research is to estab-
lish evaluation metrics that are both actionable and meaningful across the
constituent disciplines. This chapter concludes by identifying method-
ological opportunities for optimizing research outcomes and impacts.
Each of the recommended areas for evaluation necessarily incorporates
10An example of such a program is NSF’s Industry/University Cooperative Research
Centers Program.
11An illustrative example is the National Institutes of Health Mentored Quantitative Re-
search Development Award (K25), which serves to fund quantitatively trained researchers so
that they can learn about an area of biomedical science (see http://grants.nih.gov/grants/
guide/pa-files/PA-06-087.html). These awards require the identification of a mentor in the
substantive area and a plan for training; they provide funding for a commitment of at least
75 percent time.
OCR for page 101
PROGRAMMATIC AND INSTITUTIONAL OPPORTUNITIES 101
interdisciplinary team members to assess accurately and to guide the
potential for positive and significant impact.
One set of validation metrics would ensure that sustainability-
oriented efforts related directly to key components of sustainability and
were making significant progress on one or more of the legs of social,
economic, and environmental impacts.12 Specific metrics might include
the following: metrics for analysis of environmental impact (life cycle,
energy needed to create and execute a solution, as well as energy saved,
and so on); metrics for analysis of economic impact (cost of implementa-
tion, resources used to create intervention, externalities); and metrics for
equity and engagement across different stakeholder groups, taking into
account their interests, values, and concerns, during both design and
execution processes.
Many of these metrics involve humans, and so the assessment of
research using such metrics will often involve techniques from the social
sciences.13 They also involve techniques drawn from other fields, such as
life-cycle analysis from civil and environmental engineering. Any solu-
tion that claims to engage with and increase sustainability should be able
to make an argument that addresses possible ways in which it may also
negatively impact sustainability in any of its three overarching compo-
nents (social, environmental, and economic). Moreover, proposals for
projects that aim to make an impact should include a life-cycle analysis,
and such analyses should be accounted for in the projects’ budgets, as
such analyses are a non-trivial effort to do well.
Scale analysis is critical to most information technology designs; it
includes spatial scaling, temporal scaling, location scaling, and compu-
tational scaling. As demonstrated by the classic computer science talk
12As an example, although the challenges of sustainability are much broader than those
related to climate change alone, in that domain a measurable impact is the goal of decreasing
emissions so that carbon dioxide levels in the atmosphere are at or below a certain threshold.
Being as specific as possible in terms of the anticipated impact on sustainability is critical.
More broadly, one might consider the areas addressed in life-cycle analysis: global warming,
stratospheric ozone depletion, acidification, eutrophication, smog, terrestrial toxicity, aquatic
toxicity, human health, resource depletion, and land use. See U.S. Environmental Protection
Agency, “Life Cycle Assessment: Principles and Practice,” available at http://www.epa.
gov/nrmrl/lcaccess/lca101.html.
13This is a characteristic of much of the evaluation done in human-computer interaction
research and practice. Also very relevant here are design methodologies and approaches
that seek to account for human values: for example, Value Sensitive Design, as discussed
in B. Friedman, P.H. Kahn, Jr., and A. Borning, “Value Sensitive Design and Information
Systems,” in P. Zhang and D. Galletta (eds.), Human-Computer Interaction in Management
Information Systems: Foundations, Armonk, N.Y.: M.E. Sharpe (2006), pp. 348-372, reprinted
in K.E. Himma and H.T. Tavani (eds.), The Handbook of Information and Computer Ethics,
Hoboken, N.J.: Wiley (2008), pp. 69-101.
OCR for page 102
102 COMPUTING RESEARCH FOR SUSTAINABILITY
question “But does it scale?,” an early measure of the potential for the
impact of a solution or system relates to scale. As sensors become more
powerful (being able to measure many parameters at high frequency)
and cheaper, a massive increase in the amount of data is expected. Each
step or component of a proposed solution should be designed to work at
scale. However, scale has new connotations with respect to sustainability
that must be considered. In particular, many of the most problematic
phenomena involved in sustainability challenges play out over timescales
that are difficult for humans to comprehend, and many of the solutions
make sense only when applied at scale. Here are some of the scalability
questions that should be considered when evaluating a project:
• Spatial scaling requires articulating the minimal scale that can have
impact (for example, touch points or density, geographic coverage, and
so on).
• Temporal scaling incorporates real-time, human-time, and planning-
time considerations; duration; timescale of relevance (onset, persistence,
resilience); and the very challenging issue of addressing multiple-human
lifespan timescales.14
• Location scaling addresses the applicability of the approach across
a range of location-related contexts.
• Computational scaling refers to the tractability of the problem in
terms of data generated and in terms of the anticipated footprint of the
approach with respect to energy and dollars expended.
For each of these dimensions, the impact of a targeted innovation
may well be difficult to quantify precisely. But it is the responsibility of
the researcher at least to estimate and justify the anticipated, first-order
measurable savings and efficiency improvements, or mitigated damages,
from its realization. Is the proposed approach fundamental infrastruc-
ture? A game changer? The introduction of new computing technologies
and concepts should be coupled with impact assessment (positive and
negative) and follow-up study/assessment along with plans to integrate
and iterate learning. Quantifying sustainability results as contributed by
computational methods is a daunting challenge, especially given the cur-
rent lack of data for real-world systems. Focused efforts toward creating
publicly available data repositories that could be used to compare the
effect of methods on the performance metrics chosen may prove useful
in some domains. Without such data, there is little common ground for
14B. Friedman and L.P. Nathan, Multi-lifespan information system design: A research
initiative for the HCI community, Proceedings of the 2010 ACM Conference on Human Factors
in Computing Systems, New York: ACM Press (2010), pp. 2234-2246.
OCR for page 103
PROGRAMMATIC AND INSTITUTIONAL OPPORTUNITIES 103
systematically comparing different methods and their potential benefits. It
is particularly important for researchers to estimate outcomes and for the
community to develop ways to assess impact, as these steps may have a
dramatic impact on whether or not an approach can be appropriated and
applied in other contexts.
CONCLUSION
Meeting the challenges of sustainability, as noted in Chapter 1, will
require more than information technology, applications of clever technol-
ogy, and computer science research. Indeed, at the heart of many global
sustainability challenges are questions of resource consumption and stan-
dards of living. (See Box 3.3.) Nevertheless, the committee believes that
BOX 3.3
Toward an Information-Rich, Sustainable Future
Numerous analyses make clear that resource consumption is at the heart of
many global sustainability challenges. At the same time, populations around the
world are striving to improve their standard of living—and despite the efficiency
improvements that also accompany development, that has inevitably meant in-
creased resource consumption.
Efforts to improve efficiencies and substitute more sustainable for less sus-
tainable materials and methods are what underlie much of the discussion in this
report. However, there may be a broader sense in which information technologies
and computational approaches can alleviate or mitigate the problem. Efforts to shift
standard-of-living metrics from resource-intensive to information-intensive have
the potential to be a significant lever in addressing global sustainability, although
such shifts will increase the need for ever-“greener” information technology solu-
tions themselves. In an increasingly information-rich and carbon-restricted world,
finding ways to use information so that it both enhances perceptions and realities
of standard of living and reduces resource consumption will be critical.
Examples of shifting to information-rich, less resource-intensive lifestyles
include adjustments to transportation practices such as: information infrastructures
that transform the convenience and trust of shared and alternative transportation
modalities instead of private automobiles; improved technology in vehicles; trans-
portation displacement such as telecommuting, social media, and e-commerce;
and so on.
Although such examples emphasize opportunities to shift what counts as
improvements in the standard of living for individuals, ultimately it is the policy
choices and decisions, at local, regional, and federal levels, that will determine how
many, if any, of these shifts are possible. Thus, organizational and governmental
actions and decisions will have significant impact on whether a shift to information-
intensive choices can happen in order to produce a shift in the way that society
operates, to engender more sustainable outcomes.
OCR for page 104
104 COMPUTING RESEARCH FOR SUSTAINABILITY
CS and IT research has deep and fundamental contributions to make to
these challenges. This chapter has argued for a bottom-up approach to
research that values application-driven results while also supporting the
iterative process that eventually leads to more universally useful contri-
butions. The committee has argued for a series of validation metrics that
explicitly explore the true impact of a piece of work in the arena of sus-
tainability. Such validation metrics should include those that deal directly
with humans, economics, and ecosystems and those metrics that engage
with the concept of scale (a good first-order proxy for the universality that
may not yet be present).
Information technology is at the heart of nearly every large-scale
socioeconomic system—financial systems, manufacturing systems, energy
systems, and so on. One important consequence, which has been the focus
of this report, is that advances in IT have become critical enablers of
change in these systems. The goal of this report has been to shine a spot-
light on areas where information technology innovation and computer
science research can help, and to urge the computer research community
to bring its approaches and methodologies to bear on these pressing
global challenges.
