Today I would like to present an argument by the German philosopher Gregor Betz, In defence of the value free ideal, published in European Journal for Philosophy of Science (2013) 3:207–220.
Betz makes the case for maintaining the ideal of a value free science. He thus touches upon issues we have discussed many times on this blog, mainly under the labels of advocacy or honest brokering. Betz is a philosopher and approaches the question from a different angle. Conceptual analysis and logical inference are major tools for the job. Some may find the argument lengthy or tedious, but I find it illuminating.
The notion of a value free science was perhaps most famously put forward by Max Weber. Betz introduces it as follows:
The ideal of value free science states that the justification of scientific findings should not be based on non-epistemic (e.g. moral or political) values. It derives, straightforwardly and independently, from democratic principles and the ideal of personal autonomy: As political decisions are informed by scientific findings, the value free ideal ensures—in a democratic society—that collective goals are determined by democratically legitimized institutions, and not by a handful of experts (cf. Sartori 1962, pp. 404–410). In regard of private decisions, personal autonomy would be jeopardized if the scientific findings we rely on in everyday life were soaked with moral assumptions (see Weber 1949 , pp. 17–18). [Betz, p. 207].Weber's ideal has been contested by various authors and Betz summarizes the criticisms in the following way (I have shortened somewhat):
There are two versions of the methodological critique, which object to the value free ideal in different ways while sharing a common core. A first variant of the critique argues that the value free ideal cannot be (fully) realized, a second variant states that it would be morally wrong to realize it. The common core of both versions is a kind of underdetermination thesis. It claims that every scientific inference to policy-relevant findings involves a chain of arbitrary (epistemically underdetermined) choices:
Thesis 1 (Dependence on arbitrary choices) To arrive at (adopt and communicate) policy-relevant results, scientists have to make decisions which (i) are not objectively (empirically or logically) determined and (ii) sensitively influence the results thus obtained.
Thesis 2 (Decisions value laden) Decisions which (i) are not objectively determined and (ii) sensitively influence policy-relevant results (to be adopted and communicated) are inevitably based—possibly implicitly—on non-epistemic value judgments. From Theses 1 and 2, it follows immediately that science cannot be free of nonepistemic values.
Thesis 3 (Value free science unrealizable) Scientists inevitably make non-epistemic value judgments when establishing (adopting and communicating) policy-relevant results.
Thesis 4 (Policy-relevant results consequential) The policy-relevant results scientists arrive at (adopt and communicate) have potentially (in particular in case they err) morally significant societal consequences.
Thesis 5 (Moral responsibility) Any decision that is not objectively determined and has, potentially, morally significant societal consequences, should be based on nonepistemic value judgments (instead of being taken arbitrarily).
Thesis 6 (Value free science unethical) Scientists should rely on non-epistemic value judgments when establishing (adopting and communicating) policy-relevant results.
Betz then goes on to deconstruct this line of argument, showing that scientists are not forced to adopt a value judgement but have the possibility to communicate the uncertainties clearly to decision makers (you have to read the paper for the technical details). Betz then outlines some methodological principles which enable scientists to remain value neutral, yet communicating their knowledge in responsible ways. Then he gives the following example:
For illustrative purposes, we consider a ‘frank scientist’ who tries to comply with the methodological recommendations outlined above ... She might address policy makers along the following lines: “You have asked us to advice you on a complicated issue with many unknowns. We cannot reliably forecast the effects of the available policy options, which you’ve identified, in a probabilistic—let alone deterministic— way. Our current insights into the system simply don’t suffice to do so. However, we find that, if policy option A is adopted, it is consistent with our current understanding of the system (and hence possible) that the consequences CA1, CA2, . . . ensue; but note that we are not in a position to robustly rule out further effects of option A not included in that range. For policy option B, though, we can reliably exclude this-and-this set of developments as impossible, which still leaves CB1, CB2, . . . as a broad range of future possible consequences. These results are obviously not as telling as a deterministic forecast, but they represent all we currently know about the system’s future development. We, the scientists, think it’s not up to us to arbitrarily reduce these uncertainties. On the contrary, we think that democratically legitimized decision makers should acknowledge the uncertainties and determine—on normative grounds—which level of risk aversion is apt in this situation. Finally, the complex uncertainty statement I have provided above is as well confirmed as other empirical statements typically taken for granted in policy making (e.g., that plutonium is toxic, coal burns, earth’s atmosphere comprises oxygen, etc.). That is because all we relied on in establishing the possibilistic predictions were such well-confirmed results.”
This sounds quite like the Honest Broker role, identified by Roger Pielke Jr. in his book with the same title. In the following section Betz argues that the IPCC follows such a model and that - while still not perfect and not always applied - this is on the right path:
It is universally acknowledged that the detailed consequences of anthropogenic climate change are difficult to predict. Centurial forecasts of regional temperature anomalies or changes in precipitation patterns, let alone their ensuing ecologic or societal consequences, are highly uncertain. So, no wonder that climate scientists, in particular those involved in climate policy advice, have reflected extensively on how to deal with these uncertainties. A recent special issue of Climatic Change is further evidence of the attention climate science pays to uncertainty explication and communication. Some of the special issue’s discussion is devoted to the IPCC Guidance Note on Consistent Treatment of Uncertainties (Mastrandrea et al. 2010 ). The current Guidance Note , which is used to compile the Fifth Assessment Report (5AR), is a slightly modified version of the Guidance Note for the 4AR. The Guidance Note may serve as an excellent example for how the very statements and results scientists articulate and communicate are modified and chosen in the light of prevailing uncertainties.
Based on Mastrandrea, Betz constructs the following typology:
A) A variable is ambiguous, or the processes determining it are poorly known
or not amenable to measurement.
B) The sign of a variable can be identified but the magnitude is poorly known.
C) An order of magnitude can be given for a variable.
D) A range can be given for a variable, based on quantitative analysis or expert judgment.
E) A likelihood or probability can be determined for a variable, for the occurrence of an event, or for a range of outcomes (e.g., based on multiple observations, model ensemble runs, or expert judgment).
F) A probability distribution or a set of distributions can be determined for the variable either through statistical analysis or through use of a formal quantitative survey of expert views.
Again, Betz comments as follows:
From state A) to state F), the scientific understanding gradually increases, and the statements scientists can justifiably and reliably make become ever more informative and precise. If, as is the case in state A), current understanding is very poor, scientists might simply report that very fact, rather than dealing with significant inductive risks when inferring some far-reaching hypothesis (as the methodological critique has it). Importantly, the statement that a process is poorly understood, that the evidence is low and that the agreement amongst experts is limited—such a statement itself does not involve any practically significant and policy-relevant uncertainties (contra premiss P2’). The Guidance Note thus provides a blueprint for making uncertainties fully explicit and avoiding substantial inductive risks.
This is not to say that the framework provided by the IPCC is perfect and flawless. In addition, I’m not claiming here that the actual IPCC assessment reports consistently implement the Guidance Note and articulate uncertainties in a flawless way. But even if the guiding framework and the actual practice might be improved upon, the IPCC example nonetheless shows forcefully how scientists can articulate results as a function of the current state of understanding and thereby avoid arbitrary (methodological) choices. This effectively defeats the methodological critique of the value free ideal.
I think this paper re-asserts in a thorough way the Weberian ideal of a value-free science which proceeds by other means than we have seen here on Klimazwiebel. I have defended the ideal on occasions but based on a utilitarian logic ('scientists will lose their credibility if they give up that ideal'). I think Hans von Storch has expressed such a defence with a similar rationale. So in this sense, perhaps we can learn from a philosopher of how to reason about a fundamental value of science, its value neutrality.