This work emphasizes the potential of computational modeling in philosophy, particularly in the field of philosophy of science. By analyzing the innovative use of computational methods by philosophers and the contributions of Paul Thagard to the computational philosophy of science, this work demonstrates how computational modeling can aid in our understanding of complex philosophical questions surrounding scientific inquiry. Specifically, how the use of parallel computation in characterizing group rationality in science has a more philosophically superior and reliable account of the social process of scientific inquiries. This chapter will provide a summary of the key findings from the research analysis. It will also propose a solution to the research problem, highlighting areas that require further exploration and investigation. Additionally, it will offer suggestions for how computational modeling can be more widely adopted in philosophical research, emphasizing the benefits it can provide for advancing our understanding of complex philosophical problems. Finally, this chapter will conclude by reflecting on how this work has contributed to our knowledge and understanding of the intersection of philosophy and computation and by reiterating the importance of computational modeling as a tool for philosophers in their investigations.

5.1. Summary of Findings

Thagard discusses the methodological issues that arise in scientific research, particularly in the context of group rationality. He suggests that a uniform approach where all individuals work in the same way may not be the most effective method for scientific advancement and that a division of labor and method may be more appropriate. Thagard also acknowledges the issue of scientists' attachment to their hypotheses, which can be both a motivator for thorough research and a potential source of conservatism or fraud. To investigate these methodological issues, Thagard proposes the use of computer simulations to model group operations. By programming different methodological styles, such as those of Kuhnian and Popperian scientists, and comparing their performance, insights could be gained into the nature of scientific methodologies and group rationality. Thagard also suggests the possibility of a mixed group with different methodological styles, which could provide further insights. Overall, Thagard's proposal of using computer simulations to investigate group rationality in science is a novel and potentially valuable approach to understanding the complexities of scientific research. The results of such simulations could contribute to the theory of group rationality in science and provide important insights into the nature of scientific methodologies.

5.2. Recommendations

Thagard suggests that scientific theories can be understood as computational systems that consist of interconnected concepts, rules, and problem-solving strategies. This perspective offers an alternative to traditional accounts of theories as sets of axioms or logical structures. By treating scientific theories as computational systems, we gain insight into how scientists use them to solve problems in their everyday work, drawing on schemas developed from past problem solutions. According to Thagard, scientific theories are more than just abstract collections of ideas or principles. Rather, they are actively used by scientists to guide their research and solve specific problems. This perspective emphasizes the importance of problem-solving in scientific inquiry and suggests that theories are best understood in terms of their practical applications.

This view of scientific theories also helps to explain the complex functions of Kuhn's paradigms. Kuhn argued that scientific progress occurs through shifts in dominant paradigms, which shape the way that scientists approach problems and interpret data. From a computational perspective, paradigms can be seen as overarching frameworks that organize the concepts, rules, and problem-solving strategies of a particular scientific community. As scientists work within these paradigms, they develop schemas based on past problem solutions, which allow them to more effectively solve new problems within the same paradigm. At a more philosophical level, the computational view of scientific theories also sheds light on how theoretical explanations work. According to this perspective, an explanation can be understood as a problem-solving strategy that uses explanatory schemas to make sense of a particular phenomenon. In other words, an explanation is not just a static statement of facts or principles, but an active process of problem-solving that draws on the resources of a particular theory. Overall, the computational view of scientific theories offers a dynamic and practical perspective on scientific inquiry, emphasizing the importance of problem-solving and the role of theories as practical tools for guiding research.

5.3. Contribution to Knowledge

I mention as a final merit of this work that it makes possible a reunification of scientific and philosophical methods since computational modeling can aid scientific communities in their understanding and execution of scientific inquiries. Thagard rightly pointed out that an understanding of scientific knowledge will require the representation of observations, laws, theories, concepts, and problem solutions. For a full description of the roles that these play in such activities as problem-solving and discovery, it is necessary to use representations with more structure than a logical model would admit. As proffered by Thagard, PI's concepts require much internal structure because of the ways they duster information together and spread activation through the system during problem-solving. The expressive equivalence of two systems does not imply procedural equivalence and procedural questions are crucial for understanding the development and application of scientific knowledge. This demonstrates the philosophical superiority and reliability of parallel computational accounts of theories in the social process of scientific inquiries. It’s evident at this juncture that in the philosophy of science, computer models offer a much broader range of representational techniques than are found in traditional logic, probability, and set theory, allowing expansion to take into account the important roles of imagery, analogy, and emotion in human thinking. Just as significant, computer models make possible investigation of the dynamics of inference, not just abstract formal relations. Far from being oxymoronic, computational philosophy offers powerful new tools for investigating knowledge, reality, and morality.

5.4. Suggestions for Future Research

The process of scientific inquiry is not a straightforward linear path of forming hypotheses from data and testing them through experiments. It involves multiple interacting sub-processes such as problem-solving, theorizing, and experimentation. As the computational philosophy of science advances, there is potential to construct more complex models that can better capture these interactions. Additionally, there is a need to consider the social nature of science, where investigators with different methods and motivations work together, and to develop parallel models that can account for this complexity. Scientific inquiry is a dynamic process that involves various interacting sub-processes and social factors, and efforts should be underway to develop more comprehensive models that can better capture this complexity. This is evident in Thagard’s belief that “an enormous amount of work remains to be done to develop a computationally detailed, historically accurate, psychologically plausible, and philosophically defensible account of the structure and growth of scientific knowledge”.

5.5. Conclusion

In conclusion, this essay has explored the potential of computational modeling as a tool for philosophical research, with a focus on its application in the philosophy of science. By reviewing the works of various philosophers who have used computational methods to investigate fundamental philosophical questions, it has become evident that computational modeling can provide valuable insights into complex systems and aid in the development of philosophical views. The work of Paul Thagard, in particular, has been instrumental in advancing our understanding of scientific reasoning and the nature of scientific knowledge. His use of computational modeling has shed light on the cognitive processes involved in scientific inquiry and has provided a framework for understanding how theories are generated and evaluated. Thagard's contributions to the philosophy of science demonstrate the potential for computational modeling to contribute to philosophical understanding and provide a philosophical superiority and reliability of computational accounts of theories. Overall, this essay emphasizes the need for wider adoption of computational modeling in philosophy as a whole and highlights the potential for computational modeling to contribute to philosophical inquiry. The use of computational methods in philosophy can provide valuable insights into complex systems and aid in the development of philosophical views, particularly in the philosophy of science. As computational resources continue to advance, it is expected that the use of computational modeling in philosophy will become increasingly prevalent and impactful.