​I work mainly in

​Inconsistency in science
​since 2014

​​My research aims at providing philosophical understanding of the phenomenon of inconsistency toleration in empirical sciences, specifically the toleration of inconsistencies between theory and observation. 

In order to do so, my research has focussed on different facets of the phenomenon of inconsistency toleration in the empirical sciences, some of which include:

• The epistemological side: In particular, I have studied the role of ignorance when explaining the rational toleration of contradictions in the sciences.
• The formal side: I have addressed the question: how can certain paraconsistent formal tools account for cases of inconsistency toleration in empirical sciences. In order to answer to such question, I have provided a detailed analysis of three cases on inconsistencies from the empirical sciences, and I confront such cases with three paraconsistent formal tools, namely, Chunk and Permeate, Partial Structures and Adaptive Logics.
• The historical side: I have researched in detail four historical episodes that illustrate the toleration of contradictions between theory and observation.

(PHILOSOPHICAL) UNDERSTANDING ​+ HPS 
​​SINCE 2017

I’m primarily concerned with the possibility of achieving philosophical understanding in the philosophy of science --even if using defective (historical) information to support philosophical theses. I have focussed on the question What could be the value of historically inaccurate reconstructions for the philosophy of science? I have argued that philosophically-biased reconstructions, even if not historically accurate, can play a highly important epistemic role for the development of the philosophy of science, namely, to enhance our understanding of philosophical theses about science. ​​

UNDERSTANDING DEFECTIVE INFORMATION IN SCIENCE AND PHILOSOPHY
since 2019

​It is undeniable that scientific understanding is a fundamental component of any successful scientific enterprise; understanding a theory allows scientists to find new domains of application for it, and understanding an empirical domain makes it possible to build new theoretical approaches to that domain. Unfortunately, and despite the value of scientific understanding for the development of science, it is a phenomenon that still remains largely unexplored in the philosophical literature.

In particular, while much current scientific practice makes use of defective (partial, vague, conflictive, inconsistent, and false) information (cf. Arenhart, J. R. B. and Krause 2016; Bueno 1997, 1999, 2006, 2011, 2017, da Costa 2000; da Costa and French 2002, 2003; da Costa and Krause 2014; Priest 2002) philosophers of science have struggled to explain how, if possible, scientists can achieve understanding when using this type of information.

In this respect two main stories have been told. On the one hand, there have been those who characterize understanding as an epistemic achievement that comes only after having obtained explanatory knowledge; this type of understanding has received the name of explanatory understanding (cf. Kvanvig 2003; Grimm 2006, 2014; Morris 2012; Strevens 2013, 2017; Kelp 2014; Sliwa 2015; Lawler 2016, 2018). If understanding is essentially explanatory, it would be available only if the content of the scientist’s beliefs is true, and it would be impossible to achieve understanding via the use of defective (partial, vague, conflictive, inconsistent, and false) information. On the other hand, if scientific understanding is non-explanatory, it could be achieved through the use of defective information; and, although, according to this view, cases of explanatory understanding would be legitimate cases of understanding, they would not by so in virtue of the satisfaction of the factual condition of knowledge or the previous acquisition of causal knowledge (Pettit, 2002; Elgin 2004, 2007, 2017; De Regt y Dieks 2005; De Regt 2009, 2015; Khalifa 2013; De Regt and Gijsbers 2017; Le Bihan 2017, Wilkenfeld 2017; Wilkenfeld, Plunkett, and Lombrozo 2018).
​
My working hypothesis is that scientific understanding is an extremely complex phenomenon that encompasses a range of types of understanding that include explanatory understanding (under some circumstances) as well as non-explanatory understanding (under different circumstances).

Through my research, I will propose a model of scientific understanding that recognizes different types and degrees of understanding, some of which, I argue, are achievable through the use of defective information. In parallel, I will seek to extend the results of this research to cases of philosophy of science; this will shed light on both scientific understanding and philosophical understanding.

I will explore the possibility of relating different types of defects (partiality, vagueness, conflict, inconsistency and falsehood) to different degrees and types of understanding. The novelty of such an exploration would be that, even if non-explanatory understanding has been strongly advocated by epistemologists of science, the view still lacks a fine-grained analysis of the different ways in which understanding is available for the epistemic agents although the necessary conditions for explanation remain unsatisfied. My proposal consists in providing such a more fine-grained analysis.

REFEREED ARTICLES​

[1] "The ignorance behind inconsistency toleration" [Preprint here]
​in S.I. Knowing the Unknown Synthese. 2020.

[2] “Are you a selective-realist dialetheist without knowing it?”
Revista Colombiana de Filosofia de la Ciencia  19(38). 2019. https://doi.org/10.18270/rcfc.v19i38.2411).

[3] ​“Keeping Globally Inconsistent Scientific Theories Locally Consistent”
(joint with Michéle Friend) 
In: Carnielli W., Malinowski J. (eds) Contradictions, from Consistency to Inconsistency. Trends in Logic (Studia Logica Library), vol 47. Springer; pp 53-88, 2018.

[4] “The possibility and fruitfulness of a debate on the principle of non-contradiction”
(second author | Joint with Luis Estrada- González) 
In: Carnielli W., Malinowski J. (eds) Contradictions, from Consistency to Inconsistency. Trends in Logic (Studia Logica Library), vol 47. Springer; pp 33-51, 2018.

[5] “May the Reinforcement Be with You: On the Reconstruction of Scientific Episodes”
(first author| Joint with Luis Estrada-González)  
Journal of the Philosophy of History 12 (2):259–283 (2018)  

[6] “Holism, Inconsistency Toleration and Inconsistencies between Theory and Observation”
Humana Mente Journal of Philosophical Studies 32:117-147, 2017.

​PEER-REWIED CONFERENCE PROCEEDINGS 

[1] “Inconsistent Reasoning in the Sciences and Strategic-Logical Pluralism”
forthcoming in Southwest Philosophical Studies (2020).


[2] “The (in)consistency of the bernoullian infinitesimals” (in Spanish)
(Second author, joint with Luis Estrada-González) 
Fernando Macías Romero et al., eds., Memorias del Segundo Congreso Internacional de Matemáticas y sus Aplicaciones, Benemérita Universidad Autónoma de Puebla: México, pp. 195-200, 2015.​

Translation to English  (by Claudia Tanus-Pimentel)
​​

​

EDITED COLLECTION

[1] Beyond Toleration? Inconsistency and Pluralism in the Empirical Sciences​ 
(joint with Luis Estrada-González)
Special issue of Humana.Mente Journal of Philosophical Studies, Issue 32 - August 2017.

​
DISSERTATION

[Doctoral] Applications of Paraconsistent Tools in the Study of Tolerance towards Contradictions between Theory and Observation
Defended October 31, 2019.

​
​WORK IN PROGRESS

the value of historically inaccurate reconstructions for the philosophy of science

ABSTRACT

The value of historically inaccurate reconstructions for the philosophy of science: Some reflections on understanding, exemplifying and reinforcing.

​
Here I propose that philosophers can still benefit (in a non-trivial way) from philosophically biased and historically inaccurate historical reconstructions. In particular, I argue that historical reconstructions, even if philosophically biased, can play another equally important role: to enhance our understanding of philosophical theses about science by clarifying some of their concepts or applications.


Handout

mAKING SENSE OF THE USE OF DEFECTIVE INFORMATION IN THE SCIENCES
​(With otávio bueno)

ABSTRACT

Although much current scientific practice makes use of big data and scientists have struggled to explain precisely how do big data and machine learning algorithms actually work, they still trust some significant chunks that these datasets contain.  Here we clarify how, from a reliabilist point of view, we can make sense of the continued trust placed by scientists in defective information consistently with ascribing rationality to them. We use a Partial Structures approach to explain that the methods that scientists use for working with extremely large data sets are trustworthy as they preserve (and increase) the  veracity of the information –even if scientists cannot disclose all the mechanisms of the methods that they use. We illustrate this with a case study from cosmology.

THE Challenge of the illusion:
Understanding defective theories
(with moisés macías-bustos)

ABSTRACT
​
Here we content that when scientists report having understood a defective theory, even if clearly false or impossible, their claim might be legitimate. We argue that scientists understand a defective theory if they can recognize the theory's underlying pattern(s) and if they can reconstruct and explain what is going on in specific cases of defective theories as well as consider what the theory would do if not-defective –even before finding ways of fixing it.  

A MODAL READING OF NEWCOMB'S PROBLEM
(JOINT WITH GABRIELLE RAMOS-GARCÍA)
Spanish

Este artículo tiene dos objetivos principales; primero, ofrecemos  un análisis filosófico del Problema de Newcomb (en adelante, PNcomb), que es uno de los objetos de interés más importantes de la teoría de la decisión. Segundo, discutimos brevemente algunas de las condiciones bajo las cuales herramientas formales como la lógica modal son de ayuda en el estudio de las problemáticas de la decisión racional, en particular, PNcomb.

Is there anything special about the ignorance involved in Big
Data practices?

​It is a fact that the larger the amount of defective (vague, partial, conflicting, inconsistent) information the more challenges scientists face when working with it. Here, I address the question of whether such challenges are of the same kind when working with ordinary-sized datasets than when working with Big Data. In order to respond to such a question, I focus on one particular epistemic challenge that comes naturally when dealing with large amounts of information, namely, ignorance. I submit that the ignorance that emerges when using Big Data in the empirical sciences is quite unique: it is a limit case of ignorance of theoretical structure with reliable consequences.  I illustrate this with a case study from cosmology.