Thinking as an Engelsian

In this essay, I address the question of how Marxism influences our thought and action as radical intellectuals by focusing on Friedrich Engels’ work, Dialectics of Nature, the way it has been taken up in critical environmental studies and how Engels’ thinking has influenced me. In later life, Engels made important contributions on topics that are distinct from Marx's economic work. He attempted to apply dialectical methods to the “natural sciences” and he also used his knowledge of anthropology to produce a study of the historical origins of private property and women's oppression. In both cases he has been accused of adopting a positivist approach that lacks the emphasis on human agency found in Marx. Here, I challenge this view by showing how Engels’ work has been of use to practicing scientists – particularly to Richard Levins and Richard Lewontin in their book The Dialectical Biologist. I further argue that this understanding of dialectics is fully commensurable and actually advances an approach to Marxism that is based on human self-emancipation. As an undergraduate biology student these scientists inspired me with their approach to their subject as well as their activism. The essay concludes with some brief thoughts on the importance and limitations of adopting a Marxist method when considering socio-environmental change.

The applicability of mathematics in computational systems biology and its experimental relations

In 1966 Richard Levins argued that applications of mathematics to population biology faced various constraints which forced mathematical modelers to trade-off at least one of realism, precision, or generality in their approach. Much traditional mathematical modeling in biology has prioritized generality and precision in the place of realism through strategies of idealization and simplification. This has at times created tensions with experimental biologists. The past 20 years however has seen an explosion in mathematical modeling of biological systems with the rise of modern computational systems biology and many new collaborations between modelers and experimenters. In this paper I argue that many of these collaborations revolve around detail-driven modeling practices which in Levins’ terms trade-off generality for realism and precision. These practices apply mathematics by working from detailed accounts of biological systems, rather than from initially idealized or simplified representations. This is possible by virtue of modern computation. The form these practices take today suggest however Levins’ constraints on mathematical application no longer apply, transforming our understanding of what is possible with mathematics in biology. Further the engagement with realism and the ability to push realistic models in new directions aligns well with the epistemological and methodological views of experimenters, which helps explain their increased enthusiasm for biological modeling.

La agroecología como conocimiento necesario para transformar la mutua determinación sociedad–naturaleza

Los agroecosistemas son, en principio, un tipo particular de ecosistemas orientados a la producción —a partir de la tierra— de bienes materiales útiles a los seres humanos. Su estudio dista de ser simple. Consideremos que para la ecología, incluso al margen de los agroecosistemas, el estudio de los ecosistemas y de los diferentes niveles de organización de las comunidades bióticas y su interacción con el medio abiótico planteaba ya un reto tal que en los años sesenta del siglo XX el ecólogo Richard Levins (1966) hablaba ya de la necesidad de un nuevo programa de investigación al que Levins y Lewontin nombraron <em>biología de poblaciones</em> (Levins 2004; Lewontin 2004).

Agroecology as necessary knowledge to transform the mutual determination nature-society

At first glance, agroecosystems are a specific kind of ecosystems organized towards the production from the land of useful goods for human beings. The study of agroecosystems is far from being simple. Just consider that even outside from the study of agroecosystems, the study of natural ecosystems has been challenging for ecology. As an answer to the task of studying the different levels of organization of biotic communities and their interaction with abiotic factors, Richard Levins (1966) stressed the need for a new research programme, later called <em>population biology</em> (Levins 2004; Lewontin 2004). Such a research programme should be able to simultaneously address the different levels of heterogeneity (physiological, genetic, and related to age structure) of systems in which many species interact with each other. In these systems demographic changes occur that affect the very structure of the communities and alter the pattern of environmental heterogeneity.

Engineering Model Independence

According to population biologist Richard Levins, every discipline has a “strategy of model building,” which involves implicit assumptions about epistemic goals and the types of abstractions and modeling approaches used. We will offer suggestions about how to model complex systems based upon a strategy focusing on independence in modeling. While there are many possible and desirable modeling strategies, we will contrast a model-independence-focused strategy with the more common modeling strategy of adding increasing levels of detail to a model. Levins calls the latter approach a ‘brute force’ strategy of modeling, which can encounter problems as it attempts to add increasing details and predictive precision. In contrast, a model-independence-focused strategy, which we call a ‘pluralistic strategy,’ draws off of Levins’s use of an assemblage of multiple, simple and—critically—independent models of ecological systems in order to do predictive and explanatory analysis. We use the example of model analysis of levee failure during Hurricane Katrina to show what a pluralistic strategy looks like in engineering. Depending on one’s strategy, one can deliberately engineer the set of available models in order to have more independent and complementary models that will be more likely to be accurate. We offer advice on ways of making models independent as well as a set of epistemic goals for model development that different models can emphasize.

On reciprocal causation in the evolutionary process

Recent calls for a revision of standard evolutionary theory (SET) are based in part on arguments about the reciprocal causation. Reciprocal causation means that cause-effect relationships are obscured, as a cause could later become an effect andvice versa. Such dynamic cause-effect relationships raise questions about the distinction between proximate and ultimate causes, as originally formulated by Ernst Mayr. They have also motivated some biologists and philosophers to argue for an Extended Evolutionary Synthesis (EES). The EES will supposedly expand the scope of the Modern Synthesis (MS) and Standard Evolutionary Theory (SET), which has been characterized as gene-centred, relying primarily on natural selection and largely neglecting reciprocal causation. I critically examine these claims, with a special focus on the last conjecture and conclude – on the contrary– that reciprocal causation has long been recognized as important both in SET and in the MS tradition, although it remains underexplored. Numerous empirical examples of reciprocal causation in the form of positive and negative feedbacks are now well known from both natural and laboratory systems. Reciprocal causation have also been explicitly incorporated in mathematical models of coevolutionary arms races, frequency-dependent selection, eco-evolutionary dynamics and sexual selection. Such dynamic feedbacks were already recognized by Richard Levins and Richard Lewontin, well before the recent call for an EES. Reciprocal causation and dynamic feedbacks is one of the few contributions of dialectical thinking and Marxist philosophy in evolutionary theory, and should be recognized as such. I discuss some promising empirical and analytical tools to study reciprocal causation and the implications for the EES. While reciprocal causation have helped us to understand many evolutionary processes, I caution against uncritical extension of dialectics towards heredity and constructive development, particularly if such extensions involves attempts to restore Lamarckian or “soft inheritance”.

Acercamiento a la ciencia desde la asimetría, la irregularidad y la subversión

<p class="p1">Entrevista a Richard Levins, un hombre que nunca aceptó las divisiones entre las disciplinas, ni entre la ciencia y otras actividades del ser humano, como la política, por ejemplo.</p>

Approaching Science From Asymmetry, Irregularity and Subversion

<p class="p1">An interview with Richard Levins, a scientist who never accepted divisions between disciplines, nor between science and other human activities, such as politics, for example.</p>