The possibility of unexpected physical, biological, and social impacts of global scale environmental change is an inhibiting factor in creating policy responses. Participants in this session worked to define and clarify terms relevant to responding to global change, as well as create best practices for characterizing and communicating uncertainty. A categorization of types of unexpected events (surprises) was developed as part of the workshop.
Keywords: uncertainty; communication; climate change and variability
Overview & Relevance:
Senator Bill Bradley once asked a panel of atmospheric scientists at a hearing on issues of global climate change, “just what kind of surprises did you have in mind?” After the obligatory, “We can’t know a surprise in advance,” those attending admitted that they did have some inklings: radical ocean current flip-flops, permafrost melting (and possible methane release), synergisms between habitat fragmentation and species migration in response to global changes, super hurricanes, environmental refugees and attendant political instability, breakthroughs in alternative energy system prices, unexpected disease vectors, and strongly stabilizing cloud feedback effects, were some of the examples mentioned. The list is highly speculative, formidably non-linear and very interdisciplinary. Yet, such possibilities can be uttered — and other possible candidates for global change surprises were uncovered when people of achievement and insight, ranging across physical, biological, and social scientific disciplines were brought together in the congenial atmosphere of an Aspen Global Change Institute summer session.
The topic of anticipating global change surprises is highly relevant as we consider how to respond to global change in the face of uncertainty. The possibility of unexpected physical, biological and social impacts of global scale environmental change is a principal uncertainty in estimating the urgency of implementing policy responses to the advent or prospect of global change.
Content:
In addition to identifying and discussing a variety of candidates for global change surprises, the participants in this session worked through defining and clarifying relevant terms, and developed a typology of surprise that recognizes risk, uncertainty and ignorance.
Clarifying Terms
Surprise and uncertainty are often confused in the literature and in public discourse; various meanings are used within different communities and cultures.
Definitions
Risk The condition in which the event, process, or outcomes and the probability that each will occur is known. Issue: In reality, complete knowledge of probabilities and range of potential outcomes or consequences is not usually known and is sometimes unknowable.
Uncertainty The condition in which the event, process, or outcome is known (factually or hypothetically) but the probabilities that it will occur are not known. Issue: The probabilities assigned, if any, are subjective, and ways to establish reliability for different subjective probability estimates are debatable.
Surprise The condition in which the event, process, or outcome is not known or expected. Issue: How can we anticipate the unknown, improve the chances of anticipating, and, therefore, improve the chances of reducing societal vulnerability?
Working definition
Use of a strict definition of surprise logically entails that we cannot anticipate the event, process, or outcome, because the very act of anticipation implies some level of knowledge. Assessments designated as “surprises,” however, indicate that the events, processes, and outcomes so registered were, in fact, knowable in one manner or another. This second type of “surprise” — a broad use of the term — is that from which the global-change community may learn much.
Following Holling (1986: 294), the AGCI group adopted the following working definition of this second type —
Surprise is a condition in which perceived reality departs qualitatively from expectations.
Logic of Anticipating Surprise
1. Given the second meaning, it is possible to anticipate a subset of surprises.
2. For example, complex systems, chaos, and other such theories provide a conceptual and analytical basis for understanding that surprises will occur, and a variety of methods (e.g., simulations, backcasting) and assessments that facilitate seeking and finding surprises.
3. Coupled with experience, this understanding permits the identification of potential arenas wherein surprise may take place.
4. This identification may (should) inform the public and policymakers of the issues, and thus potentially allow reduced vulnerability and enhanced environmental and societal resilience to surprise.
5. The probabilities that suspected “surprises” will take place within a specified arena are generated on a subjective basis (or by objective methods or models that rest on subjective assumptions), and vary significantly by individual, community and culture.
Who is Surprised and Why?
1. Surprise is dependent on expectations, and thus we must analyze how expectations are formed by individuals and groups.
2. This view implies that the degree of surprise depends on the extent to which reality departs from expectations and on the salience of the problem (e.g., hazards).
3. Expectations reside not only in the individual, but with groups, communities, or cultures, such as experts, policymakers, managers, and educators, who can share common ranges of expectations that are generated by group dynamics, leaders, and signal processors.
4. In many cases, surprise lies in the policy/managerial mindset and response to an unexpected or improbable (lowly anticipated: e.g., Three Mile Island) event.
5. A variety of factors contribute to this subcategory of surprise (#4), including: differences of opinions among the expert community; fit with broader policy agendas; and vested interests of agencies or groups to maintain a particular view.
6. Factors that may contribute to surprise (of our second type) among the science and policy communities are those involved under conditions of systems complexity and connectedness. Integrated systems modeling, for example, informs that (i) one surprise may lead to another because of sub-system coupling and other such issues and (ii) cascading surprises may emerge.
Typological Map
There are many possible typologies of surprise (and uncertainty) (e.g., Brooks 1986; Timmerman 1986). Adapting from Faber, Manstetten, and Proops (1992), the AGCI meeting produced a typology that recognizes risk, uncertainty, and ignorance. Here, risks are possible (usually undesirable) outcomes whose probability and existence are known. Uncertainty characterizes outcomes that are known to be possible but whose probabilities are not known. Ignorance, the main subject of the typology, is the most intractable: we are ignorant when we cannot or do not know a possible outcome. Following this typology and definition, ignorance may be where the most significant surprises lie. (It should be noted, however, that some do not make such strong distinctions among these three sources of surprise but see each as a variant on the same basic insight that outcomes are indeterminate.)
Ultimately all ignorance might be reducible, but much of it is very hard to overcome. Part of this hard-to-reduce ignorance stems from epistemology — the rules that we think govern how the world works and the language and symbols we use to describe what we think and observe. Some people use the term “paradigm” to describe those rules, relationships, symbols, and language. (Some point out that “epistemological ignorance” can be a form of “closed ignorance” because epistemological blinders lead to an unwillingness or unwitting inability to consider alternatives.) The other part of this “hard”-to-reduce ignorance is intrinsic to the phenomenon at hand. Some phenomena may simply be unpredictable, at least from the technologies and analytical perspective now in existence. Notably, systems characterized by chaos are currently thought to be unpredictable in detail — for example, detailed weather forecasts six months in advance are not possible, no matter how accurate the initial state of the weather condition is known because of chaotic dynamics of the atmosphere. And yet, the general character of some chaotic-like systems can be better understood, permitting models of them and, hence, forecasts of their impacts (e.g., El Ni ño or ENSO events). A further example of phenomenological ignorance is a change in the underlying forces of a system, producing markedly different observed outcomes.
This typology is helpful because:
• it makes a distinction among risk, uncertainty, and surprise;
• it also makes clear that phenomenological surprise is only one category of ignorance; and,
• it suggests that many surprises are easily reducible,
• whereas others are blocked by epistemological blinders that create expectations that exclude some categories of outcomes and, hence, surprise.
Fitting the Map From the Bottom Up
A series of surprises pertinent to global environmental change were presented at the workshop. To each candidate surprise (and in some cases highly uncertain outcomes that were perceived by many as surprises) was attached the sources attributed to them as understood by our group. A few cases of phenomenological ignorance were presented, particularly those in which the technology of data retrieval outpaced the analysis of data (e.g., misreading remotely sensed imagery led to exaggerated estimates about the spatial scale of land-cover changes; or erroneous assumptions about outlier values of stratospheric ozone delayed detection of the Antarctic ozone hole). Most of the cases, however, suggested sources of surprise in global environmental change may be closely aligned with the following:
narrowness of “paradigm” (epistemological ignorance)
• organizational goals and structure of organizational decisionmaking not consistent with the problem (closed ignorance; epistemological ignorance)
• organizational goals in conflict with the outcome (closed ignorance)
• purposeful obfuscation and blocking (closed ignorance)
• rigid common frameworks (epistemological ignorance — frameworks/mindsets that impede effective use of normal science and learning)
Scientific Versus Societal Surprise
At any time, a number of new events or surprises vie for the attention of society as a whole. They enter a process that Kasperson and colleagues (1988) describe as social amplification and attenuation, whereby the processing of the event or discovery by information and response systems either strengthens or weakens the signal value to managers, policymakers, and publics. Thus, some genuine scientific surprises fail to be taken up by the mass media, watchdog groups, or policymakers and fail to make it onto the societal agenda. Other surprises, perhaps less salient to scientists, undergo substantial amplification in signal value due to intense coverage in the mass media, lobbying by critics or environmental groups, connection to social movements, or concern on the part of policymakers or regulators. Thus, it is important to distinguish between scientific and social surprise and to evaluate how events interact with societal processes to amplify or attenuate the perceived significance of the surprise to managers, social institutions, and publics.
Improving the Anticipation of Scientific
Surprise
The sources of global-change surprise noted above point to several ways of improving the anticipation of the arenas or domains of surprise.
1. Encourage and integrate the role of synthesis and synthesizers — appreciating “putting the puzzle together” and searching for connections across problem domains, disciplines, and perspectives.
2. Focus a larger fraction of the research effort on “outlier” outcomes (e.g., applying methods to sample the opinions of a broad range of knowledgeable experts as to the likelihood of a wide range of imaginable outcomes).
3. Support work at the edges (and across edges) of conceptual and problem areas.
4. Promote process- as well as product-oriented research and encourage multiple disciplines and communities to communicate and integrate their knowledge about global-change problems.
5. Insure the following attributes of research discourse and funding that have been insufficiently appreciated to date:
A. skeptical welcoming of advocacy science/scientists and of the airing and professional evaluation of unconventional views
B. multiplicity and constructive duplication of research domains among approaches and institutions.
6. Work backwards from posited future states to identify events or processes that might happen along the way: backcasting scenarios or reconstruct past scenarios in alternative ways to examine what might have happened (e.g., Brooks 1986).
7. Encourage the “strategic paradigm” as well as the “efficiency paradigm” to build resilience into social and environmental systems.
Preparing for Surprise: Beyond the Science
It is, of course, the negative and potentially catastrophic surprises that are of particular concern. Managers and social institutions are not helpless to these surprises simply because specific events and outcomes cannot be predicted reliably or even (perhaps) anticipated. What can be done is to increase the resilience and adaptability of receptors (human and ecological) that are at risk, thereby decreasing the sensitivity to the impacts of the unexpected or uncertain perturbations. Actions aimed at increasing the resilience and adaptability of potentially affected systems are noted below. They do not represent recommendations of AGCI but are provided as examples of the broader ranging amplifications of surprise and global change.
1. Diversifying economic productive systems: the tendency towards increased economic specialization carries the risk of vulnerability to controls (e.g., markets or absentee landlords) well beyond the local area which can have both positive and negative impacts on local resilience to environmental perturbations.
2. Avoidance of technological monocultures: reliance on a single technology, such as nuclear power, may be vulnerable to environmental or other perturbations with negative impacts on the economy.
3. Strengthening the broader entitlement structures: providing robust safety nets to respond to unforeseen events is a critical part of resilience.
4. Adaptive management systems: organizational theory suggests that different management systems have different capacities for dealing with surprise; those doing better are characterized by openness, participation of all parties, and flexibility, while those faring less well are characterized by command-and-control systems.
5. Disaster coping systems: improving designs of early-warning, monitoring, and alerting systems, and strengthening the capability of private and public sectors to respond rapidly to potential disasters should be encouraged.
6. Organizational memory and social learning: measures that improve memory and the ability to learn from surprises improve overall resilience to vulnerability to surprise.
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Event Co-Chair