Pragmatic Approach to Laws

In her essay “Pragmatic Laws”, Mitchell rejects the normative and paradigmatic approach to laws. As she said, she is not going to define what law is, but to discuss the “understanding” of the usage of generalization in science. Firstly, Mitchell discusses about the traditional view of laws, the normative approach. In normative approach, scientists need to find or create strict laws that can be applied generally. By inputting different initial conditions, these laws will generate specific outcomes that meet the phenomena of natural world. If people cannot find such laws in biology, then it means there are no laws in biology. The normative approach ignores the degree of contingency. Mitchell argues that many laws, especially biology laws, do not have clear distinction between university and contingency. Next, Mitchell discusses the paradigmatic approach. In this approach, scientist will compare the generalization in biology with the laws in physic or other science to see if there is a match. If the generalizations in biology are like the law in other science, then there are laws in biology. The paradigmatic approach fail to define laws in some “unsystematize space” where no physical laws can be referred.

Finally, Mitchell puts forward her pragmatic approach to biological laws. Since the biological world is relatively complex and involves numerous initial factors, she argues that we should rather focus on the functions of biological generalizations and how they acts as “laws.” If generalizations can provide good prediction of natural phenomena, then we should regard them as “laws”, or at least “law-like.” Mitchell argues that pragmatic laws should be regulate by degree of accuracy, level of ontology, simplicity and cognitive manageability, as well as the stability and strength.

I agree that in biology science, it is difficult to generalize perfect strict  physics laws.  However, I would regard Mitchell’s pragmatic approach to laws as permissive.

Firstly, the pragmatic approach is permissive because it allows “unscientific approaches.” Mitchell ignored one of the key functions of laws of natural, which is that they help and direct science researching path. By saying “unscientific approach”, I mean to speak of those immature and not well-formed generalizations, such as Ptolemy’s theory, which predicts the planets’ orbit but it is false. Strict normative definition of law can exclude many “unscientific approaches” from science research. The reason we want to have laws is because we need guidelines in scientific research, but the pragmatic approach allows these “unscientific approach” to function as laws, which will bring confusion. Without a clear definition of the laws, how can we distinguish lawful generalizations from accidental truth? 

Admittedly, Mitchell gives precise restriction to her pragmatic laws by arguing that contingent generalizations should be valued and restricted by some important parameters, including the degree of accuracy, level of ontology, simplicity, and cognitive manageability plus stability and strength (in Leuridan’s paper). Mitchell argues that if they cannot meet the standards, these generalizations will be considered as not useful and should not be used as laws. However, her regulations are still permissive. This leads to my next objection against pragmatic laws.

Mitchell’s pragmatic approach allows contingent mathematical model become laws. She allows the contingent initial condition to join with the statement, which is excluded from normative approach. In normative approach, we do not have initial conditions, or say, all this initial conditions is built from the Big Bang, from the beginning of the universe. Every normative law is in the structure of “all P is Q.” While pragmatic laws allows “I→[if P then Q].”  This kind of permissiveness will lead to the arbitrariness of the formulation of laws. If scientists find out that a contingent statement I1→[if P then Q] does not function well, they can easily add more initial conditions until their contingent generalization ( in the form of I1, I2 I3… In→[if P then Q])becomes more stable and fits the natural phenomenon. Actually this is the process of mathematical modeling. Mathematical models are precise generalizations that can help scientists to predict or analyze nature or social phenomenon, within certain contingent conditions. In the pragmatic approach, these mathematical models can be fit in Mitchell’s regulations, especially stability and strength. People may suspect the simplicity of mathematical models. However, many models do reflect the natural simplicity. In economics, all we have is these generalizations, which we call them mathematical models. We have Taylor rule in monetary economics, which can precisely predict the relationship between nominal interest rate, GDP, and inflation rate. It can perfectly meet Mitchell’s regulations, since it is simple, statistical precise, empirical, and stable in a certain timescale. However, why economists never call their economics models laws? Firstly, models are merely generalizations. There must be precise strict laws underneath. In biology and economics, a phenomenon may involve with numerous laws and factors. It is impossible to find out all the laws and unrealistic to use complex combinations of strict laws to predict phenomena. Thus, we have to use contingent generalizations. Secondly, contingent generalizations fail to meet the functions, as I have mentioned previously. Taylor rules may have good prediction, but since we are not sure the complex mechanism, it is hard for us to consider it as a law. Therefore, if Taylor rules cannot be seen as a real law, then how can we consider those biological generalizations as laws?

In addition, it is not necessary to count “lawful statements” as laws. Against the criticisms that pragmatic approach is permissive, Leuridan argues that contingent generalizations do play the role of laws in the history of science. He also argues that these generalizations “fit the actual science practice” (326). Leuridan made a mistake by arguing contingent generalizations are useful when they are being regarded as laws. I agree with Mitchell and Leuridan’s recognition that contingent generalizations do have the function as laws of natural. I do realize contingent generalizations do help scientists develop their science research and explain the natural. However, the classification of contingent generalizations as laws does not contribute to their function in science. By this, I mean that it is true that excluding contingent generalizations from being “true laws” do not stop scientists from using them in scientific research. Mathematical models that generalize a complex phenomenon can function well without being regarded as a law. Economist never say their supply and demand model as a “price law”, but it still help us to predict price for several centuries.

In conclusion, Mitchell successfully recognize complexity when explaining biological phenomenon and the useful functions of contingent generalization, but her pragmatic approach fails to notice the basic functions of laws.