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Impact Framing

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Catastrophe scenarios make policy fail – reduce their probability to zero.

Rescher, ’83 (Nicholas, 1983, Professor of Philosophy, University of Pittsburg, Risk: A Philosophical Introduction to the Theory of Risk Evaluation and Management, University Press of America, p. 39-40)

But in decision theory there are two different, more pressing reasons for dismissing sufficiently improbable possibilities. One is that there are just too many of them. To be asked to reckon with such remote possibilities is to baffle our thought by sending it on a chase after endless alternatives. Another reason lies in our need and desire to avoid stultifying action. It’s simply “human nature†to dismiss sufficiently remote eventualities in one’s personal calculations. The “Vacationer’s Dilemma†of Figure 1 illustrates this. Only by dismissing certain sufficiently remote catastrophic possibilities as outside the range of real possibilities – can we avoid the stultification of action on anything like standard decision-making approach represented by expected-value calculations. The vacationer takes the plausible line of reviewing the chance of disaster as effectively zero, thereby eliminating that unacceptable possible outcome from playing a role by way of intimidation. People generally (and justifiedly) proceed on the assumption that the probability of sufficiently unlikely disasters can be set at zero; that unpleasant eventuations of “substantial improbability†can be dismissed and taken to lie outside the realm of “real†possibilities.

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best card, use with systemic impacts.   

 

Linear predictions are impossible. Prefer solving existing problems.

Deug Whan Sa, 04 Dong-U College, South Korea, (“CHAOS, UNCERTA I N T Y, AND POLICY CHOICE: UTILIZING THE ADAPTIVE MODEL,† International Review of Public Administration, vol. 8, no. 2, 2004, scholar)RK

 

Third, the characteristic is the existing Newtonian determinism theory which presumes linear relations where things proceed from the starting point toward the future on the thread of a single orbit. Thus, it also assumes that predictions of the future are on the extended line of present knowledge and future knowledge is not as unclear as the present one (Saperstein 1997: 103107), and that as similar inputs generate similar outcomes, there will be no big differences despite small changes in initial conditions. However, chaos theory assumes that the outcome is larger than the input and that prediction of the future is fundamentally impossible.3 Hence, due to extreme sensitivity to initial fluctuations and non-linear feedback loops, small differences in initial conditions are subject to amplifications and eventual different outcomes, known as ‘chaos.’4 Chaos is sometimes divided into strong chaos and weak chaos (Eve, Horsfall and Lee 1997: 106); and goes through a series of orbit processes of close intersections and divisions. In particular, weak chaos is found in the limits that account for the small proportion inside a system, while strong chaos features divisions at some points inside a system, which lead to occupation of the entire system in little time. CHAOS, UNCERTAINTY AND POLICY CHOICE 1. Review of Existing Policy Models Social scientists have tried to explain and predict policy matters, but never have generated satisfactory outcomes in terms of accuracy of predictions. There could be a variety of reasons for this inaccuracy in prediction, but one certain reason is that policies themselves are intrinsically governed by uncertainty, complexity and chaos in policies that produce many different outcomes though they are faced with the same initial internal states, the same environments, and governed by the same causal relationships

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best card, use with systemic impacts.   

 

Linear predictions are impossible. Prefer solving existing problems.

Deug Whan Sa, 04 Dong-U College, South Korea, (“CHAOS, UNCERTA I N T Y, AND POLICY CHOICE: UTILIZING THE ADAPTIVE MODEL,† International Review of Public Administration, vol. 8, no. 2, 2004, scholar)RK

 

Third, the characteristic is the existing Newtonian determinism theory which presumes linear relations where things proceed from the starting point toward the future on the thread of a single orbit. Thus, it also assumes that predictions of the future are on the extended line of present knowledge and future knowledge is not as unclear as the present one (Saperstein 1997: 103107), and that as similar inputs generate similar outcomes, there will be no big differences despite small changes in initial conditions. However, chaos theory assumes that the outcome is larger than the input and that prediction of the future is fundamentally impossible.3 Hence, due to extreme sensitivity to initial fluctuations and non-linear feedback loops, small differences in initial conditions are subject to amplifications and eventual different outcomes, known as ‘chaos.’4 Chaos is sometimes divided into strong chaos and weak chaos (Eve, Horsfall and Lee 1997: 106); and goes through a series of orbit processes of close intersections and divisions. In particular, weak chaos is found in the limits that account for the small proportion inside a system, while strong chaos features divisions at some points inside a system, which lead to occupation of the entire system in little time. CHAOS, UNCERTAINTY AND POLICY CHOICE 1. Review of Existing Policy Models Social scientists have tried to explain and predict policy matters, but never have generated satisfactory outcomes in terms of accuracy of predictions. There could be a variety of reasons for this inaccuracy in prediction, but one certain reason is that policies themselves are intrinsically governed by uncertainty, complexity and chaos in policies that produce many different outcomes though they are faced with the same initial internal states, the same environments, and governed by the same causal relationships

Actually, that could probably take out one's own impacts, even if they're systemic. One could make the argument that just because it happened before doesn't mean it'll continue to happen. That, of course, isn't a complete takeout, but still.

 

Here are some that I have.

As a policy maker, you should evaluate low probability impacts at zero

Herbeck & Katsulas 92 – Dale A., Professor of Communication and Director of the Fulton Debating Society at Boston College & John P., Debate Coach at Boston College

[October 29-November 1, “The Use and Abuse of Risk Analysis in Policy Debateâ€, Paper presented at 78th Annual Meeting of the Speech Communication Association, Chicago, IL, Available Online via ERIC Number ED354559, p. 10-12]

First, and foremost, we need to realize that some risks are so trivial that they are simply not meaningful. This is not to argue that all low probability/high impact arguments should be ignored, but rather to suggest that there is a point beneath which probabilities are meaningless. The problem with low probability arguments in debate is that they have taken on a life of their own. Debate judges routinely accept minimal risks which would be summarily dismissed by business and political leaders. While it has been argued that our leaders should take these risks more seriously, we believe that many judges err in assessing any weight to such speculative arguments. The solution, of course, is to recognize that there is a line beyond which probability is not meaningfully evaluated. We do not believe it is possible to conclude, given current evidence and formats of debate, that a plan might cause a 1 in 10,000 increase in the risk of nuclear conflagration.17 Further, even if it were possible, we need to recognize that at some point a risk becomes so small that it should be ignored. As the Chicago Tribune aptly noted, we routinely dismiss the probability of grave impacts because they are not meaningful: It begins as soon as we awake. Turn on the light, and we risk electrocution; several hundred people are killed each year in accidents involving home wiring or appliances. Start downstairs to put on the coffee, and you're really asking for it; about 7,000 Americans die in home falls each year. Brush your teeth, and you may get cancer from the tap water. And so it. goes throughout the day -- commuting to work, breathing the air, working, having lunch, coming home, indulging in leisure time, going back to bed.18 Just as we ignore these risks in our own lives, we should be willing to ignore minimal risks in debates. Second, we must consider the increment of the risk. All too often, disadvantages claim that the plan will dramatically increase the risk of nuclear war. This might be true, and still not be compelling, if the original risk was itself insignificant. For example, it means Hale to double the probability of nuclear war if the original probability was only 1 in one million. To avoid this temptation, advocates should focus on the initial probability, and not on the marginal doubling of the risk claimed by the negative.

As a judge, you must not base decisions on worst-case scenarios but rather balanced risk assessment

Rescher 83 – Nicholas, Professor of Philosophy at University of Pittsburgh, Chairman of the Philosophy Department, Director of the Center for Philosophy of Science, Honorary degrees from 8 universities on 3 continents, Doctorate in Philosophy from Princeton

[Risk: A Philosophical Introduction to the Theory of Risk Evaluation and Management, p. 50]

The “worst possible case fixation†is one of the most damaging modes of unrealism in deliberations about risk in real-life situations. Preoccuption about what might happen “if worst comes to worst†is counterproductive whenever we proceed without recognizing that, often as not, these worst possible outcomes are wildly improbable (and sometimes do not deserve to be viewed as real possibilities at all). The crux in risk deliberations is not the issue of loss “if worst comes to worst†but the potential acceptability of this prospect within the wider framework of the risk situation, where we may well be prepared “to take our chances,†considering the possible advantages that beckon along this route. The worst threat is certainly something to be borne in mind and taken into account, but it is emphatically not a satisfactory index of the overall seriousness or gravity of a situation of hazard.

Low probabilities should be dismissed as zero risk

Rescher 83 – Nicholas, Professor of Philosophy at University of Pittsburgh, Chairman of the Philosophy Department, Director of the Center for Philosophy of Science, Honorary degrees from 8 universities on 3 continents, Doctorate in Philosophy from Princeton

[Risk: A Philosophical Introduction to the Theory of Risk Evaluation and Management, p. 50]

But in decision theory there are two different, more pressing reasons for dismissing sufficiently improbable possibilities. One is that there are just too many of them. To be asked to reckon with such remote possibilities is to baffle our thought by sending it on a chase after endless alternatives. Another reason lies in our need and desire to avoid stultifying action. It’s simply “human nature†to dismiss sufficiently remote eventualities in one’s personal calculations. The “Vacationer’s Dilemma†of Figure 1 illustrates this. Only by dismissing certain sufficiently remote catastrophic possibilities as outside the range of real possibilities – can we avoid the stultification of action on anything like standard decision-making approach represented by expected-value calculations. The vacationer takes the plausible line of reviewing the chance of disaster as effectively zero, thereby eliminating that unacceptable possible outcome from playing a role by way of intimidation. People generally (and justifiedly) proceed on the assumption that the probability of sufficiently unlikely disasters can be set at zero; that unpleasant eventuations of “substantial improbability†can be dismissed and taken to lie outside the realm of “real†possibilities.

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What about a card that frames the debate in a way that makes saving ANY lives the most important?

well with the scenarios you've described, reading cards that talk about how important saving lives is probably wont be super strategic, because even a low-prob big impact will simply outweigh. 

 

If youre winning the probability debate, you can probably just make an analytic. 

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