Portfolio

The document analyzes digital transformation in Mexico’s agricultural sector, emphasizing its relevance for improving sustainability, climate resilience, and institutional efficiency in rural areas. Divided into two sections, the first addresses key concepts of digitalization, digital divides, regional strategies, and available digital tools, while the second explores specific technologies such as artificial intelligence, the Internet of Things, blockchain, cloud computing, and software development applied to agriculture. It also presents recommendations for implementing digital solutions that facilitate access to information, promote social inclusion, and strengthen the integration of information systems in agricultural production and marketing.

Reinforcement learning is a promising artificial maximizes a particular reward by mapping states to actions in a non-deterministic environment, intelligence area which relies on computational simulations where an agent finds a policy that applications in optimization problems. Here, we apply Q-learning, a popular model-free with learning algorithm to a representative ecological problem: the sustainable harvest of a reinforcement which follows a logistic growth rate by modeling it as a valid python gymnasium environment, population contributing to the generation of new environments which represent real-world situations and may lead to decision making. We implemented three different scenarios in which the agents determined an optimal policy guided by different reward signals: a greedy agent, which harvests as much as possible; a conscious agent, which seeks to harvest as much as possible while maintaining high population numbers; and a biocentric agent, which harvests only what is needed to avoid the population to exceed the carrying capacity of the environment. By initializing the population size to 100, with a growth rate of 1 and a carrying capacity of 200, the biocentric agent had the largest accumulated harvest, suggesting that a sustainable strategy is more profitable in the long term. Reinforcement learning is a very promising approach for decision making in many ecological real-world situations, where population management is a very straightforward application.

Envisioning trajectories towards sustainability encompasses enacting significant changes in multiple spheres (i.e., infrastructure, policy, practices, behaviors). These changes unfold within the intricate landscapes of wicked problems, where diverse perspectives and potential solutions intersect and often clash. Advancing more equitable and sustainable trajectories demands recognition of and collaboration with diverse voices to uncover meaningful synergies among groups striving to catalyze substantial change. Projects of this nature necessitate the exploration of varied tools and methodologies to elicit, convey, and integrate ideas effectively. Creating spaces for reflexivity is essential for catalyzing more meaningful impact as individuals engage in discussions aimed at sharing and questioning the coherence of their projects while forging synergies, identifying common objectives, and planning long-term outcomes. We present the initial phase of an endeavor in which we developed a software that elicits causal networks based on mapping relations between projects’ actions and outcomes. To illustrate our approach, we describe the results of using this software within collaborative workshops with groups spearheading projects initiated by a government entity in Mexico City. By adapting elements of the Theory of Change model, this software transcends the dominant linear project logic by guiding participants in designing causation networks that unveil how different projects can articulate to identify potential common elements and find new possibilities for coordination among initiatives. We discuss the potential of such software application as a dynamic tool to guide and promote reflection and coherence when crafting projects that aim to more meaningfully address sustainability problems.

This document (and its attached files) represents an effort to create a database for the Central and Neovolcanic Axis Regional Directorate (DRCEN) of the National Commission of Natural Protected Areas (CONANP) in Mexico. The result is a proposal for a maintainable relational database designed to support effective, data- and evidence-based decision-making, as well as the generation of outputs such as reports, dashboards, and visualizations. It was developed by Salvador Garcilita (founder of Bioalgoritmia) during his tenure as an advisor for the German Development Cooperation (GIZ).