Curriculum

1. Rationale

The creation of the Bachelor's in Complex Systems at the University of Buenos Aires responds to the need to educate graduates able to understand, model, build and intervene in problems whose structure requires bringing together knowledge from different fields. A significant share of today's scientific, technological, economic, institutional and social challenges unfolds in systems marked by many components and actors, interdependence, feedback, nonlinearity, adaptation, uncertainty and emergent effects.

In this framework, complexity is both a property of certain systems and an epistemological problem. It refers to the forms of organization and interaction that produce behavior hard to anticipate from analyzing the parts in isolation, and to the conditions under which those systems can be known, represented and acted upon. Understanding a complex system means recognizing relationships among components, scales, processes and levels of organization, and examining the questions, models and perspectives from which it is approached.

Every process of knowing involves selecting, abstracting and building representations. Models make certain aspects of reality intelligible by identifying relevant relationships, regularities and mechanisms, but their value also depends on recognizing their assumptions, scope and limits. The study of complexity therefore requires combining analytical precision with the ability to reconstruct relationships between dimensions that simplification separates. This means distinguishing levels of analysis without isolating them, formulating explanations compatible with uncertainty and bringing together knowledge produced through diverse perspectives and methods.

The complex thought developed by Edgar Morin (1990) offers an epistemological formulation especially relevant to this perspective. It calls for knowledge able to connect what specialization tends to fragment, while recognizing uncertainty, multidimensionality and the links between knowledge, action and responsibility. Complexity thus appears as a demand for articulation: understanding the parts, their relationships and the wholes they belong to without reducing one to the other.

The theory of complex systems developed by Rolando García (2006) provides a particularly fruitful framework for turning this perspective into interdisciplinary research. From this approach, certain problems require constructing objects of study that integrate heterogeneous processes and different levels of organization. Delimiting the system, identifying its components and relationships, selecting observables and formulating questions are central moments of the research process itself. Interdisciplinarity thus acquires a precise methodological meaning: organizing the production of knowledge around problems whose structure requires the articulation of specialized knowledge.

This epistemological perspective meets developments from the scientific theory of complex systems. Herbert Simon (1962) showed that physical, biological, social and artificial systems can exhibit hierarchical structures and forms of organization whose understanding requires identifying subsystems, levels and significant relationships. Representation and decomposition are fundamental tools for making highly complex systems analyzable, provided attention is kept on the relationships that organize the behavior of the whole.

This perspective is equally relevant to the analysis of social, economic and institutional systems, where interdependence among actors, feedback, path dependence and uncertainty limit the explanatory power of strictly linear or reductive models. Addressing these systems requires forms of analysis and decision able to recognize the plurality of actors and levels, the unintended effects of interventions and the way systems themselves change as a result of the actions taken on them (Innerarity, 2020).

The program therefore adopts a broad and rigorous conception of complexity. Mathematics provides languages to represent structures, relationships and dynamics; physics and biology offer models to understand processes, constraints, adaptation and organization; probability, statistics and the decision sciences provide tools to work with uncertainty, evaluate evidence, compare alternatives and select courses of action; computing makes it possible to build, simulate and experiment with systems; and economics, the social sciences, institutional analysis, epistemology and ethics make it possible to study incentives, norms, conflicts, meanings, power relations and conditions of legitimacy.

In this framework, the proposal sits at the intersection of the complexity sciences, systems science, the decision sciences and the fields concerned with designing and building systems. From the complexity sciences it takes the study of interaction, emergence, adaptation, networks and nonlinear dynamics; from systems science, modeling and the analysis of relationships among components, levels and environments; from the decision sciences, tools to evaluate evidence, uncertainty, alternatives and courses of action; and from the fields of systems design and construction, the ability to turn knowledge and decisions into technological systems, products, organizations, institutions and interventions that can be implemented and evaluated.

This articulation rests on solid disciplinary training. Interdisciplinary research and intervention require understanding which questions each field can answer, which assumptions organize its methods and which limits its models have. Training in complexity therefore demands the ability to work in two movements at once: going deeper into specific languages and tools, and connecting knowledge produced from different perspectives.

Complex systems also pose particular problems for action. Every intervention changes relationships, incentives, constraints and behavior, and can produce feedback or consequences that are hard to anticipate. Decision-making then requires formulating problems well, setting objectives, producing and evaluating evidence, comparing alternatives, considering the responses of other actors and revising courses of action in light of their results. For this reason, the program integrates modeling and analysis with decision theory, institutional design, strategy, construction and evaluation.

The ability to build is a central dimension of this education. Building means turning problems and knowledge into models, technological systems, products, services, organizations, institutions or interventions that can be implemented, evaluated and revised. Building makes it possible to confront representations with operating conditions, produce new evidence and identify relationships or constraints that may remain invisible in the initial analysis. Understanding, deciding and building are thus part of an iterative process of formulation, intervention, evaluation and learning.

The spread of artificial intelligence and of other technologies able to automate cognitive, analytical and productive tasks creates new conditions for that process. These tools expand the possibilities for modeling, programming, analysis and generating alternatives, and at the same time make the abilities to formulate problems, set objectives, evaluate evidence, recognize limits and take responsibility for decisions more important. University education must make it possible to understand and use these technologies, critically evaluate their results and decide under what conditions it is appropriate to incorporate them into systems and interventions.

Uncertainty, and the impossibility of fully anticipating the consequences of an intervention, give responsibility particular weight. Acting on complex systems requires assessing risks, identifying expected and unintended effects, establishing oversight mechanisms and keeping revision possible. Epistemology, ethics, culture and legitimacy are a substantive part of the proposal because they make it possible to examine what can be known, how much confidence a model or prediction deserves, which values and interests guide a decision and under what conditions an intervention is effective, justifiable and legitimate.

The proposal also responds to changes in how professional knowledge is produced and practiced. Growing interdependence among scientific, technological, economic, institutional and social problems calls for people able to work across specialized fields, integrate diverse knowledge and collaborate with professionals from different areas. This challenges university education to combine solid theoretical and disciplinary foundations with systematic experience of research, construction and intervention on problems that require interdisciplinary approaches.

The curriculum turns these principles into a progressive pathway. The initial stage builds mathematical, scientific, computational, historical and institutional foundations; the intermediate stage deepens modeling, probability, dynamical systems, artificial intelligence, decision-making and the analysis of institutions; and the advanced stage integrates problems of robustness, design, research, regulation, behavior, culture, strategy and responsibility. The Integrative Workshops are the backbone of this pathway and require students to draw on knowledge from different fields to formulate problems, build and test solutions, produce evidence, make decisions and critically review their results.

The University of Buenos Aires is an especially suitable setting for a program of this kind because of the breadth and diversity of its scientific, technological, economic, social and humanistic traditions. The Bachelor's in Complex Systems seeks to bring those strengths together in a proposal with its own identity, aimed at educating graduates able to work rigorously on problems that require integrating multiple scales, disciplines and forms of knowledge, and to intervene in them by combining understanding, uncertainty, decision, construction and responsibility.

2. Program and award title

Program name: Licenciatura en Sistemas Complejos (Bachelor's in Complex Systems).

Award title: Licenciado/a en Sistemas Complejos. The Spanish award title is retained; the English program name is a descriptive translation.

3. Mode of instruction

The program is taught in person, as established in Chapter A CÓDIGO.UBA I-18.

4. Graduate profile

Graduates of the Bachelor's in Complex Systems will have an interdisciplinary education that combines the complexity and systems sciences, the decision sciences, and mathematical, scientific and computational foundations with tools for modeling, data analysis, artificial intelligence, economics, decision theory, institutional analysis, epistemology and the social sciences. This education will enable them to understand and address problems whose structure involves many components, actors, scales, forms of interaction and uncertainty, and whose understanding requires bringing together perspectives and ways of knowing from different fields.

They will be able to identify and delimit complex problems, build representations and models suited to studying them, recognize the assumptions and limits of those representations and relate processes that operate at different levels of organization. They will be able to analyze dynamics marked by interdependence, feedback, adaptation, nonlinearity, emergence and path dependence, and to examine the historical, economic, institutional, social and cultural contexts in which those dynamics unfold.

Their education will enable them to produce, integrate and evaluate evidence using mathematical, statistical, computational, experimental and research tools. They will be able to formulate hypotheses, design measurement and evaluation strategies, analyze uncertainty, build scenarios and compare explanations or alternative interventions. They will also have epistemological criteria to examine the relationships between models and reality, recognize different levels and forms of knowledge and assess whether the methods used suit the nature of the problem at hand.

Graduates will be able to design, build, implement and evaluate technological and data-driven systems, use machine learning and artificial intelligence tools and critically analyze their possibilities, limitations and conditions of use. They will be able to evaluate their operation, robustness, security, reliability and effects, and understand the relationships between technical systems and the organizational, institutional and social environments in which they operate.

They will also be able to analyze institutions, organizations, markets, regulations and coordination mechanisms, attending to the incentives, interests, power relations, behaviors and conflicts involved in how they work. They will be able to formulate strategies, compare courses of action and make decisions under risk, uncertainty, ambiguity and incomplete information, considering the possible responses of other actors and the expected and unintended consequences of interventions.

The ability to build will be a central dimension of their profile. Graduates will be able to turn problems, knowledge and opportunities into models, systems, products, services, organizations, institutions or interventions that can be implemented, tested and revised. This ability will include formulating objectives, designing alternatives, producing evidence, assessing technical, economic and institutional feasibility and learning from the results obtained.

Their education will also include ethical criteria and a sense of responsibility for assessing the risks, impacts and consequences of the decisions and systems they take part in. They will be able to consider problems of legitimacy, accountability, oversight, the distribution of risks and benefits, and responsibility toward users, communities, organizations and institutions. They will be prepared to revise, modify, limit or abandon a solution when evidence, context or its consequences call for it.

Graduates of the Bachelor's in Complex Systems may work in scientific, technological, productive, business, institutional, public and social settings, taking part in research, analysis, modeling, design, development, implementation, management and evaluation. They will be able to join and coordinate interdisciplinary teams and bring together knowledge from different fields to address problems that cannot be adequately solved from a single disciplinary perspective.

In particular, they will be able to:

• analyze and model complex systems and their dynamics, recognizing relationships among components, scales and levels of organization;
• formulate and delimit problems whose understanding requires integrating perspectives from different fields;
• build, compare and evaluate models, simulations, scenarios and representations, identifying their assumptions, scope and limitations;
• produce, integrate and critically evaluate evidence using mathematical, statistical, computational, experimental and research tools;
• analyze uncertainty, risk and ambiguity and make decisions with incomplete information;
• design, develop, implement and evaluate technological systems and solutions based on data and artificial intelligence;
• analyze institutions, organizations, markets, regulations, power relations and coordination mechanisms;
• design and evaluate products, services, organizations, interventions and institutional arrangements;
• formulate strategies that connect objectives, resources, constraints, incentives and the possible responses of other actors;
• identify risks, vulnerabilities, failure modes, unintended effects and impacts associated with systems and decisions;
• design and carry out research and evaluation processes that integrate different forms of evidence and methods suited to the problem studied;
• take part in innovation and in creating new systems, products, services, organizations and initiatives;
• join and coordinate interdisciplinary teams and communicate reasoned arguments to diverse audiences;
• critically review decisions, models and interventions in light of the evidence obtained and of changes in context;
• act with intellectual autonomy, professional judgment, integrity and responsibility for the consequences of their actions.

In short, graduates will be able to understand and intervene in complex systems and problems by combining scientific knowledge, technological ability, economic, institutional and social understanding, and epistemological and ethical reflection. Their education will let them move between analyzing the parts and understanding their relationships, between disciplinary specialization and the integration of perspectives, and between producing knowledge and the responsibility that comes with applying it.

5. Scope of the degree

Graduates will be qualified to:

1. Analyze and model complex systems, integrating mathematical, statistical, computational, scientific and social tools to study their components, interactions, dynamics, uncertainties and possible scenarios of evolution.
2. Produce, integrate and evaluate evidence for understanding complex problems and for decision-making, using models, simulations, data analysis, research methods and artificial intelligence tools.
3. Formulate and evaluate strategies, alternative courses of action and interventions for problems marked by uncertainty, interdependence and multiple actors, considering constraints, incentives, risks, possible effects and implementation conditions, and anticipate scenarios and recognize emerging opportunities associated with scientific, technological, economic and institutional change.
4. Take part in the design, development and evaluation of systems, products, services, organizational processes and institutional arrangements, contributing modeling, data analysis, experimentation and evaluation tools from a systemic and interdisciplinary perspective, and contribute to creating and developing new initiatives that turn problems or opportunities into solutions that can be implemented and validated.
5. Carry out research, innovation, evaluation, transfer and advisory activities related to complex systems, systems science, the decision sciences, modeling, data analysis and artificial intelligence and their applications in scientific, technological, productive, organizational, institutional and public settings, including generating and evaluating innovative projects in contexts of transformation and change.
6. Join and coordinate interdisciplinary teams devoted to analyzing, formulating, implementing and evaluating complex projects and interventions, bringing together knowledge from different fields and contributing to building decision criteria and processes, organizing initiatives and leading innovation processes in diverse settings.

This scope allows graduates to work in scientific and technological settings; productive and service organizations; companies, startups and innovation projects; public agencies; social institutions and organizations; and teams working on data analysis and artificial intelligence, strategy and decision-making, organizational and institutional design, policy and intervention evaluation, research, consulting and interdisciplinary project development. The degree also prepares graduates to take part in creating and developing new initiatives and in projects that require anticipating change, bringing together diverse actors and knowledge, and acting in national and international contexts.

When activities involve professional tasks reserved exclusively for other degrees, graduates of the Bachelor's in Complex Systems will work within interdisciplinary teams and in accordance with the professional roles, responsibilities and supervision established by current regulations.

6. Admission requirements

Applicants must provide evidence of completed secondary education. Exceptionally, people over twenty-five (25) who do not meet this condition may enter by passing the relevant assessments established for that purpose under current regulations.

7. Program duration

The theoretical duration of the Bachelor's in Complex Systems is 4 (FOUR) years. Total instructional contact time is 2,272 (TWO THOUSAND TWO HUNDRED AND SEVENTY-TWO) clock hours: 144 (ONE HUNDRED AND FORTY-FOUR) for the Intensive Introductory Course and 2,128 (TWO THOUSAND ONE HUNDRED AND TWENTY-EIGHT) for the eight four-month terms.

8. Curriculum structure

General organization of the program

The Bachelor's in Complex Systems begins with an Intensive Introductory Course, followed by EIGHT (8) four-month terms. The pathway combines solid disciplinary foundations with progressive stages of integration, aimed at understanding, modeling and intervening in problems and systems whose structure involves many components, actors, scales, forms of interaction and uncertainty.

The curriculum combines mathematical, scientific and computational training with tools for modeling, data analysis, artificial intelligence and decision theory; knowledge of economics, institutions, regulation and behavior; epistemological, ethical, social and cultural perspectives; and systematic experience of research, design and construction. These components are dimensions of a common education and are brought together throughout the program around the study of complex systems and the problems of understanding and intervening in them.

The curriculum adopts a conception of complexity that covers both the properties and dynamics of the systems studied and the epistemological and methodological problems involved in knowing them. The program therefore aims for students to work with different levels of analysis, recognize the assumptions and limits of models, relate evidence produced by different methods and bring disciplinary perspectives together when the structure of the problem requires it.

The pathway combines disciplinary depth with progressive integration. The first terms build fundamental mathematical, scientific, computational, historical and institutional languages. The intermediate stage deepens probability, dynamical systems, the epistemology of complexity, numerical methods, decision theory, the modeling of complex systems, computational systems and institutional design. The advanced stage adds machine learning and artificial intelligence, control and robustness, product design, regulation, geopolitics, research, behavior, strategic interaction, culture and responsibility, while students develop a Final Integrative Project.

The program moves progressively toward open problems that require drawing on knowledge from different fields. Formulating problems, building and evaluating models, experimenting, producing evidence, making decisions under uncertainty, designing interventions and critically reviewing their results are abilities that run through the whole pathway.

Intensive Introductory Course

The program begins with an Intensive Introductory Course before the regular four-month terms. It aims to support entry into university life and to build a common base of knowledge, abilities and forms of reasoning relevant to the rest of the program.

The course comprises THREE (3) integrated modules: Mathematical Modeling; Scientific and Computational Thinking; and Economics, Institutions and Government. Its design introduces students from the outset to the different languages and perspectives needed to address complex problems.

Mathematical Modeling introduces tools for representing relationships, changes, structures and uncertainties through formal languages. Scientific and Computational Thinking develops initial abilities in problem formulation, argumentation, evidence assessment, abstraction and algorithmic reasoning. Economics, Institutions and Government introduces decisions, incentives, markets, the state, institutions and forms of social coordination.

Together, the three modules aim to give students from different school backgrounds a shared foundation, and to show from the start a central feature of the program, the need to combine different forms of reasoning when facing problems that cannot be adequately understood from a single perspective.

Organization of the learning pathway

For descriptive purposes, the pathway can be understood through FIVE (5) connected formative strands. These strands serve an analytical purpose and are not separate compartments or independent tracks. A single course can contribute to more than one strand, and the Integrative Workshops are the main space for bringing them together.

The strand of formal, scientific and computational foundations provides the languages and tools needed to represent, analyze and model systems of different kinds. It includes mathematics, probability and statistics, numerical methods and optimization, programming and computational reasoning, alongside physics and biology to understand dynamics, constraints, adaptive processes and interactions among components and scales.

The strand of systems, data and artificial intelligence develops the ability to understand, build and evaluate computational and data-driven systems. It includes computational systems, networks and security, machine learning, artificial intelligence, control, observability and formal methods, with attention to operation, robustness, reliability, verification and conditions of use.

The strand of economics, institutions and decision provides tools to analyze decisions, incentives, resources, rules, power and coordination mechanisms. It includes decision theory and stochastic processes, political economy, law and regulation, institutional design, game theory and mechanism design, geopolitics, finance, and the analysis of behavior and negotiation.

The strand of epistemology, complexity, culture and responsibility addresses the conditions under which knowledge about complex systems is produced, validated and used. It covers the relationships between models and reality, uncertainty, causality, the articulation of scales and perspectives, ethics, automation, culture, legitimacy and responsibility. Its role is to provide tools to critically examine both the systems studied and the ways in which they are known, represented and acted upon.

Finally, the strand of integration, research and project creation brings together the knowledge and abilities developed in the other strands. It includes the Integrative Workshops, methodological training and the design of products, systems and interventions. Its purpose is for students to turn problems into questions, models, strategies, systems or projects that can be tested, evaluated and revised.

Principles of progression and integration

The pathway follows two complementary criteria. A vertical progression gradually increases conceptual depth, student autonomy and the complexity of the problems addressed. Horizontally, courses taken in the same period are connected through problems, cases and projects that require combining knowledge from different fields.

The sequence moves from acquiring foundations toward increasingly open situations: representing and explaining; building and testing; working with uncertainty and deciding; analyzing and intervening in technical and institutional systems; researching, designing and operating under constraints; and finally developing and evaluating projects with greater autonomy and responsibility.

Disciplinary integration is built progressively. It presupposes command of specific concepts and methods and, at the same time, the ability to recognize when the nature of a problem requires connecting different levels of analysis, forms of evidence and fields of knowledge. Interdisciplinarity is thus a practice grounded in problems, not a simple juxtaposition of content from different areas.

Integrative Workshops

The EIGHT (8) Integrative Workshops structure the pathway. They provide systematic opportunities for students to connect knowledge from different fields to formulate problems, build and evaluate alternatives, produce evidence and take responsibility for their decisions.

The Workshops are spaces for producing knowledge and learning through intervention. In them, students must formulate and delimit problems; build models, systems or proposals; work with incomplete information; identify constraints, uncertainties and failure modes; justify decisions; document processes and review their results. They will foster increasing autonomy, collaborative work, argumentation, critical review and responsibility.

The sequence follows a deliberate progression:

Integrative Workshop I. Problem Formulation, Argumentation and Communication

Introduces the formulation and delimitation of complex problems and develops critical reading, argumentation, writing and oral communication. Students must recognize components, actors, relationships, scales, perspectives, constraints and uncertainties and produce a first reasoned analysis of a problem.

Integrative Workshop II. Building, Testing and Breaking Systems

Focuses on collaborative design and construction of functioning systems. Includes requirements definition, documentation, testing, instrumentation, peer review and adversarial evaluation. Students must identify failure modes, examine relationships among components and review design decisions in light of the results.

Integrative Workshop III. Decision and Operation under Uncertainty

Addresses situations requiring action with incomplete information, time constraints and significant consequences. Integrates evidence production and evaluation, diagnosis, forecasting, prioritization, coordination and decision-making, alongside retrospective review of assumptions, errors and results.

Integrative Workshop IV. Institutional Systems Design

Focuses on analyzing and designing rules, processes, mechanisms and institutions. Students must consider actors, incentives, power relations, regulatory constraints, legitimacy, capture risks, accountability mechanisms and the conditions for implementation and evaluation.

Integrative Workshop V. Critical System under Constraints

Involves designing, building or evaluating systems and interventions that operate under significant constraints and require explicit consideration of failure costs. Includes reliability, security, privacy, regulation, monitoring, model uncertainty, risk analysis, adversarial evaluation and mitigation and recovery strategies.

The THREE (3) final Workshops make up the Final Integrative Project, through which students develop a sustained project from formulation and validation to development, evaluation and public defense. The Project may take THREE (3) pathways of equivalent academic demand: research; development of a technological system; or an entrepreneurial and institutional initiative. The pathways adapt methods, outputs and forms of evidence to the nature of the problem while keeping common criteria of rigor, autonomy, documentation, critical evaluation and responsibility.

Integrative Workshop VI. Final Integrative Project I – Formulation and Validation

Aims to identify and delimit a relevant problem, review prior work, formulate questions, hypotheses and objectives and define evaluation criteria and feasibility conditions. Depending on the chosen pathway, it will include research design and evidence strategies; specification and initial testing of technological systems; or research on users or beneficiaries, prototyping and validation of needs and interventions. Technical, economic, legal, institutional and ethical constraints must be considered from this stage on.

Integrative Workshop VII. Final Integrative Project II – Development and Evaluation

Centers on developing, implementing or operating the formulated project. Students must produce results and obtain evidence of performance, validity, replicability, use, demand or impact according to the pathway; manage resources, agreements, risks and constraints; revise hypotheses and strategies in light of the results; and document decisions, difficulties and changes of direction.

Integrative Workshop VIII. Final Integrative Project III – Completion and Public Defense

The program's culminating stage of integration. It includes consolidating and evaluating the project, critically analyzing its results and limitations, considering its feasibility, sustainability, risks and consequences, and reviewing the main decisions taken. The process ends with a documented final output and its public defense before a panel.

Taken together, the sequence of Workshops moves students from formulating and understanding problems toward building, deciding, designing and intervening, until they reach the autonomy needed to develop and evaluate their own project. Each stage draws on abilities built earlier and subjects them to increasing levels of integration, uncertainty and responsibility.

Curriculum tables

The curriculum tables below set out the recommended sequence for completing the degree within its theoretical duration. The distribution across four-month terms supports both progressive learning and the intended connections among courses taken concurrently.

This organization does not in itself impose a mandatory enrollment sequence. Each student’s actual pathway may differ, and the academic conditions for taking and passing each course are determined by the prerequisite rules established in this curriculum.

Intensive Introductory Course

Total: 144 h

Passing all three modules is required before starting Term 1.

Term 1 · four months

Instructional contact time is expressed in clock hours. All courses are compulsory.

Total: 256 h

Term 2 · four months

Instructional contact time is expressed in clock hours. All courses are compulsory.

Total: 288 h

Term 3 · four months

Instructional contact time is expressed in clock hours. All courses are compulsory.

Total: 288 h

Term 4 · four months

Instructional contact time is expressed in clock hours. All courses are compulsory.

Total: 256 h

Term 5 · four months

Instructional contact time is expressed in clock hours. All courses are compulsory.

Total: 256 h

Term 6 · four months

Instructional contact time is expressed in clock hours. All courses are compulsory.

Total: 272 h

Term 7 · four months

Instructional contact time is expressed in clock hours. All courses are compulsory.

Total: 256 h

Term 8 · four months

Instructional contact time is expressed in clock hours. All courses are compulsory.

Total: 256 h

9. Graduation requirements

To obtain the award of Licenciado/a en Sistemas Complejos, students must:

• Pass the Intensive Introductory Course, comprising the THREE (3) modules established in this curriculum.
• Pass the THIRTY-NINE (39) compulsory curricular units of the program's EIGHT (8) four-month terms, EIGHT (8) of which are Integrative Workshops.
• Pass the Final Integrative Project, developed within Integrative Workshops VI, VII and VIII, in one of the pathways set out in this Curriculum. The Project must end with a documented final output and its public defense before a panel, under the conditions set by the program's regulations.
• Demonstrate advanced English proficiency through the procedures and conditions established by program regulations.

10. Required syllabus content

Content of the Introductory Course and the 39 curricular units, organized by four-month term.

Introductory Course

Mathematical Modeling

Mathematical representation of relationships, changes and structures. Variables, functions, equations and systems of equations. Proportionality, rates of change and scales. Reading and interpreting graphs. Introduction to mathematical problem formulation. Models as simplified representations: assumptions, parameters, scope and limitations. Computational tools for exploring and representing problems. Elementary problems of counting, frequency, proportion and randomness; intuitive notions of probability and risk; critical interpretation of forecasts and base rates.

Scientific and Computational Thinking

Problem formulation and delimitation. Observation, hypotheses, evidence and argumentation. Distinguishing data, evidence, interpretation and opinion. Abstraction and problem decomposition. Introduction to algorithmic thinking. Information representation and organization. Critical evaluation of sources. Uncertainty, error and result review. Written and oral communication of arguments and procedures. Introduction to forecast calibration, distinguishing uncertainty from lack of knowledge, and recording decisions and assumptions.

Economics, Institutions and Government

Actors, decisions and incentives. Introductory concepts of markets, organizations and the state. Formal and informal institutions. Rules and coordination mechanisms. Public goods, externalities and collective action problems. Power, authority and government. Relationships among economics, institutions and technology. Introduction to analyzing public problems from a systems perspective.

Term 1 · four months

1. Linear Algebra and Computing I

Vectors, matrices and systems of linear equations. Gaussian elimination and basic factorizations. Vector spaces, linear independence, bases and dimension. Linear transformations. Inner product, norm, orthogonality and projections. Geometric and computational interpretation. Implementing matrix operations with computational tools. Applications to data, networks, models and optimization problems.

2. Calculus and Analysis I

Functions of one variable. Limits and continuity. Differentiation and fundamental theorems of differential calculus. Local approximation and Taylor polynomials. Definite and indefinite integration. Fundamental theorem of calculus. Sequences and series. Introduction to formal reasoning through definitions and properties. Applications to change, growth, physics and modeling.

3. Systems Biology, Evolution and Ecology

Evolution by natural selection. Adaptation, robustness and fragility. Organization of biological systems at different scales. Regulatory networks, homeostasis and feedback. Population and community dynamics. Cooperation, competition, coevolution and symbiosis. Immune systems. Contagion dynamics and epidemiology. Biological systems as complex adaptive systems. Scope and limits of biological analogies applied to technological, economic and institutional systems.

4. History of Institutions, Technology and Power

Historical formation and transformation of states, bureaucracies, businesses, markets and infrastructures. Relationships between technology and institutional change. Industrial revolutions and transformations of work and production. Science, war, public administration and state capacity. Political, economic and technological power. Path dependence, continuity, rupture and innovation. Emergence and transformation of large-scale infrastructures and platforms.

5. Integrative Workshop I. Problem Formulation, Argumentation and Communication

Formulating and delimiting complex problems. Identifying actors, components, relationships, scales, constraints and uncertainties. Searching for and critically evaluating information. Critical reading and argumentative writing. Argument structure, identifying assumptions, fallacies, objections and rebuttals. Reasoned reconstruction of alternative positions. Preparing short reports, analytical papers and presentations. Oral communication and reasoned defense. Revision and rewriting. Initial systems analysis of a real problem.

Term 2 · four months

6. Linear Algebra and Computing II

Eigenvalues and eigenvectors. Diagonalization. Matrix factorizations and decompositions. Singular value decomposition. Least squares. Conditioning and numerical stability. Dimensionality reduction. Matrix representations of data, graphs and transformations. Applications to dynamical systems, optimization, data analysis and machine learning. Numerical solution of systems and error propagation in computational examples.

7. Calculus and Analysis II

Functions of several variables. Partial derivatives, gradient, Jacobian and Hessian. Constrained and unconstrained optimization. Lagrange multipliers. Multiple integrals and change of variables. Vector fields. Divergence and curl. Line and surface integrals. Green’s and Stokes’ theorems. Applications to physics, optimization, dynamical systems and machine learning.

8. Physics I: Mechanics, Energy and Thermodynamics

Quantities, units, measurement and orders of magnitude. Kinematics and dynamics. Newton’s laws. Work, energy, power and conservation principles. Linear and angular momentum. Oscillations and introductory continuous systems. Temperature, heat, internal energy and entropy. Principles of thermodynamics. Physical modeling. Dimensional analysis and experimental uncertainty.

9. Programming, Algorithms and Data Systems

Modular programming and data abstraction. Fundamental data types and structures. Recursion. Search, sorting and graph algorithms. Algorithmic complexity. Testing, debugging, documentation and version control. Data models. The relational model, query languages, normalization, indexes, transactions and migrations. Specifications, invariants, types, counterexamples and formal reasoning applied to programs. Introduction to the critical use of coding assistants and independent verification of results.

10. Integrative Workshop II. Building, Testing and Breaking Systems

Collaborative design and construction of a functioning system. Requirements and performance criteria. Documentation, testing, instrumentation and peer review. Auditing and adversarial evaluation of other teams’ systems. Identifying and classifying failure modes. Robustness analysis. Reporting and correcting failures. Post-incident analysis (postmortem). Reviewing design decisions and publicly defending results. Short estimation and prediction-tracking exercises; documenting uncertainty and test evidence.

Term 3 · four months

11. Physics II: Electromagnetism and Signals

Electric and magnetic fields. Electric potential. Current, resistance, capacitance and inductance. Electrical circuits. Introductory electromagnetic waves. Continuous and discrete signals. Time- and frequency-domain representations. Noise and filtering. System response. Applications to sensors, communication, measurement and physical systems.

12. Probability and Statistics

Probability spaces, conditional probability and independence. Random variables, distributions, expectation, variance and covariance. Law of large numbers and central limit theorem. Sampling and sampling distributions. Point and interval estimation. Hypothesis testing and statistical power. Bayesian updating, probabilistic prediction and calibration. Introductory regression. Measurement and selection biases. Design of controlled comparisons, randomization, internal validity and threats to inference. Monte Carlo simulation and computational reproduction of results. Introduction to experimental design and its relationship to decision-making.

13. Differential Equations and Dynamical Systems

Ordinary differential equations. Linear and nonlinear systems. State space. Equilibrium and stability. Oscillations and bifurcations. Discrete dynamical systems. Sensitivity to initial conditions. Models of growth, epidemics, control, physics and economics. Introductory numerical methods. Simulation and qualitative analysis of systems evolving over time.

14. Epistemology, Complexity and Ethics

Scientific knowledge, explanation, prediction, causality, models and idealization. Construction of objects of knowledge, abstraction and simplification. Assumptions, scope and limits of representations. Complexity, interdependence, relationships between parts and wholes, and levels of analysis. Articulation of disciplinary perspectives and interdisciplinary research. Falsificationism, paradigms, realism and instrumentalism. Relationships between models and reality. Uncertainty and the limits of inference. Correlation, explanation, understanding and judgment. Fundamental perspectives in ethics. Consequences, duties, rights, justice and responsibility. Epistemological and ethical problems related to automation and artificial intelligence systems: interpretability, bias, errors, alignment, validity, automation and the limits of delegation.

15. Integrative Workshop III. Decision and Operation under Uncertainty

Simulation of critical situations in technical, organizational and institutional systems. Producing and evaluating evidence for decision-making. Diagnosis, prioritization and team coordination. Crisis communication. Decisions with incomplete information and under time pressure. Identifying risks and consequences. Error handling, escalation, recovery and accountability. Post-incident analysis, institutional learning and process improvement. Recording forecasts and probabilities, comparing alternatives, calibration and ex post evaluation of decisions. Design of measurements and simple experiments. Recording hypotheses, assumptions and uncertainties, and revising decisions in light of the evidence obtained.

Term 4 · four months

16. Computational Systems, Networks and Security

Architecture of computational systems and services. Processes, memory, persistence and concurrency. Networks and protocols; communication between processes and services. Principles of distributed systems: replication, consistency and fault tolerance. Latency, performance and availability. Authentication, authorization, secrets management and threat models. Foundations of systems security. Testing, observability, monitoring, recovery and failure analysis. Principles of resilience and controlled degradation.

17. Numerical Methods and Optimization

Rounding and truncation errors; numerical precision, stability and conditioning. Methods for solving linear systems, nonlinear equations and integration problems. Discretization and approximation; validation of numerical results. Unconstrained and constrained optimization; convexity, optimality conditions, gradient methods and iterative algorithms. Computational formulation of real problems, sensitivity analysis and checking assumptions.

18. Decision Theory and Stochastic Processes

Decisions under risk, uncertainty and ambiguity. Preferences, utility and expected utility; scope and limits. Bayesian updating, calibration and value of information. Sequential decisions. Multi-criteria decision-making and sensitivity analysis. Heuristics, biases and expert judgment. Elementary stochastic processes: Markov chains, counting processes and simulation. Model uncertainty, deep uncertainty and robust decision criteria. Probability of ruin and distributional effects. Applications to technical, economic and institutional decisions.

19. Complex Systems

Interaction, interdependence, nonlinearity and feedback. Emergence, self-organization and adaptation. Path dependence. Relationships between local behavior and aggregate outcomes. Complex networks, cascades and propagation processes. Agent-based models. Adaptive systems in biology, economics, technology and institutions. Robustness, fragility, resilience and systemic risk. Simulation and visualization. Sensitivity analysis. Scope and limits of prediction and control.

20. Integrative Workshop IV. Institutional Systems Design

Designing rules, processes, mechanisms and institutions. Identifying actors, interests, capabilities and incentives. Cooperation, coordination and conflict. Legal and institutional constraints. Legitimacy and capture risks. Designing institutional interventions and public services. Metrics, auditing, accountability mechanisms and review and appeal procedures. Feasibility and implementation assessment. Argumentation and critical evaluation of institutional proposals. Indicators and basic strategies for evaluating interventions. Comparison groups, selection biases, counterfactuals and the limits of causal attribution.

Term 5 · four months

21. Machine Learning and Model Engineering

Foundations of supervised and unsupervised learning. Regression, classification, overfitting, regularization and generalization. Model training, validation and selection. Metrics, calibration, error analysis and distribution shift. Reproducibility and critical evaluation of results. Applications of simple models to real data and recognition of inference limits.

22. Product Design, Interaction and Strategies

Research on users, beneficiaries and contexts of use. Identifying needs and formulating hypotheses. Prototyping, interaction design and usability testing. Designing products, services and interventions. Feedback mechanisms, onboarding, error handling and user control. Positioning, adoption and distribution. Pricing, costs, unit economics and sustainability. Defining and analyzing metrics. Technical, economic and institutional feasibility. Evidence-based experimentation and iteration. Products and services based on artificial intelligence: automation, explainability and user control.

23. Control, Observability and Measurement

Modeling dynamical systems. Feedback, stability and dynamic response. Controllability and observability. State estimation. Instrumentation, signals and metrics. Monitoring, dashboards, alerts and anomaly detection. Operational experiments. Effects of metrics on the systems measured and Goodhart’s law. Introductory robust control. Parameter uncertainty and modeling errors. Evaluating strategies under disturbances. Resilience, controlled degradation, fault containment and recovery.

24. Political Economy, Law and Regulation

Markets, the state, businesses and institutions. Market failures, public goods and externalities. Distribution and economic power. Political economy of regulation. Interests, regulatory capture, state capacity and compliance. Principles of public law and economic regulation. Competition and concentration. Regulation of technologies, data, platforms, infrastructure and AI. Major traditions of economic thought and their relationships with institutions, technology and crises.

25. Integrative Workshop V. Critical System under Constraints

Designing and building systems or interventions where failure costs are significant. Defining requirements and specifications. Reliability, security, privacy, regulation and operating conditions. Technical, economic, institutional and organizational constraints. Adversarial evaluation and critical review. Risk and failure-mode analysis. Monitoring, documentation, mitigation and recovery strategies. Evaluating robustness to changing scenarios, distribution shift, model uncertainty and correlated failures. Tail risk and decisions under ambiguity. Justifying and defending technical and institutional decisions.

Term 6 · four months

26. Deep Learning and AI Systems

Deep neural networks. Backpropagation, optimization, regularization and normalization. Architectures for vision, language, sequences and multimodal data. Attention mechanisms and foundation models. Pretraining, fine-tuning and model adaptation. Retrieval-augmented generation. Systems capable of using tools and carrying out sequences of actions. Evaluation of AI models and systems. Security, alignment, oversight and governance. Data, compute, latency and cost constraints. Deployment, operation and monitoring.

27. Institutional Design Laboratory

Diagnosing real or simulated institutional problems. Decision and coordination architectures. Designing rules, procedures, incentives and governance mechanisms. Information, capabilities and power relations. Metrics, controls and accountability. Interaction with relevant actors. Assessing political, legal, operational and budgetary feasibility. Implementation, monitoring and adaptation of institutional arrangements. Presenting and defending proposals. Defining and justifying a measurement and evaluation strategy; designing pilot tests and critically analyzing the validity of the evidence obtained.

28. Formal Methods and Verification

Specifications, preconditions, postconditions and invariants. Program contracts and properties. Distinguishing testing, simulation and verification. Introduction to logic applied to systems, deductive verification and counterexample analysis. Computer-assisted tools for checking selected properties. Integrating verification techniques with development, testing and adversarial evaluation practices in Integrative Workshops.

29. Geopolitics, Technology and the International System

Power, sovereignty, dependence and interdependence in the international system. Technological and economic competition among powers. The United States, China, Europe and Latin America. Semiconductors, energy, digital infrastructure, data, critical materials and global production chains. Standards, dual-use technologies, economic controls and sanctions. Technological sovereignty, alliances and national capabilities. Systemic risks and transformations of the international order associated with new technologies.

30. Integrative Workshop VI. Final Integrative Project I – Formulation and Validation

Identifying and delimiting a relevant problem and choosing the project pathway: research, development of a technological system, or an entrepreneurial and institutional initiative. Review of prior work and formulation of questions, hypotheses and objectives. Defining evaluation criteria and feasibility conditions. Designing evidence strategies according to the pathway: research and methodological design; specifications and tests for technological systems; research on users or beneficiaries and validation of needs for initiatives. Prototyping, empirical exploration and initial validation. Defining metrics and comparison criteria. Identifying biases, assumptions and uncertainties. Analysis of technical, economic, legal, organizational and ethical constraints. Planning, documenting and justifying the proposal.

Term 7 · four months

31. Statistical and Causal Inference, and Experimental Design

Advanced statistical inference and uncertainty quantification. Problems of conceptualization, operationalization and measurement. Construct and measurement validity. Sampling, case selection and selection bias. Regression models and their assumptions. Causal identification: randomized experiments, instrumental variables, regression discontinuity, difference-in-differences and other impact evaluation methods. Observational and quasi-experimental designs. Internal and external validity, statistical power and multiple comparisons. Surveys, interviews and other evidence strategies; integration and triangulation of quantitative and qualitative sources. Replication, reproducibility, transparency, research ethics and communication of results. Applications to technical systems, organizations, public policy and integrative projects.

32. Game Theory and Mechanism Design

Static and dynamic games. Dominant strategies and best responses. Nash equilibrium. Repeated games. Cooperation, coordination, commitment and reputation. Incomplete information and signaling. Coalitions. Mechanism design. Incentive compatibility and participation constraints. Auctions, allocation, matching, voting and market design. Applications to platforms, institutions, regulation and public policy.

33. Human Behavior, Negotiation and Conflict

Cooperation, conflict, trust and reciprocity. Distributive and integrative negotiation; alternatives to negotiation, credible commitments and coalitions. Persuasion, leadership, power and asymmetries. Team dynamics, collective action, institutional cooperation and conflict. Collective decisions under pressure and uncertainty. Simulations, analysis of results and critical review of negotiation strategies.

34. Integrative Workshop VII. Final Integrative Project II – Development and Evaluation

Developing, implementing or operating the project formulated in the previous stage. Producing results and obtaining evidence of performance, validity, replicability, use, demand or impact, according to the pathway. Defining and tracking metrics and evaluation procedures. Managing resources, agreements, risks and constraints. Revising hypotheses and execution strategies in light of the evidence obtained. Testing, experimentation and reproducible documentation. Recording and analyzing decisions, assumptions, negative results and changes of direction. Presentation and reasoned defense of progress and results.

Term 8 · four months

35. Finance, Money and Banking

Financial statements, cash flows, budgets and valuation. Investment and financing decisions. Risk, return, liquidity, leverage and capital structure. Money, credit, banking intermediation and payment systems. Central banks, monetary policy, interest rates and inflation. Counterparty risk, financial contagion and systemic fragility. Crisis models, uncertainty, risk of ruin and limits of financial measurement. Critical evaluation of financial institutions and decisions in national and international contexts.

36. AI, Judgment and Responsibility

Transformations associated with the expansion of AI and automation. Capabilities and limits of contemporary AI systems. Relationships among prediction, recommendation, decision and judgment. Automation and limits of delegation. Human agency and oversight. Responsibility for decisions assisted or executed by automated systems. Alignment, institutional power and legitimacy. Accountability and review mechanisms. Case analysis and reasoned defense of positions.

37. Culture, Legitimacy and Informal Institutions

Culture, social norms and informal institutions. Formation and transmission of shared meanings. Authority, legitimacy and obedience. Trust, reputation, identities, rituals and symbols. Relationships between formal and informal institutions. Collective action, symbolic coordination and normative change. Cultural conflict. Acceptance, appropriation and resistance to technologies and interventions. Relationships among effectiveness, legitimacy and public responsibility in technical and institutional systems. Anthropological and cultural-evolution perspectives applied to institutional analysis.

38. Creation, Aesthetics and Responsibility

Creating knowledge, technologies, products, organizations and institutions. Criteria of quality, usefulness, beauty, elegance, meaning and value. Forming, justifying and discussing aesthetic and professional judgments about what one creates. Perspectives from moral philosophy and the philosophy of technology applied to creation and decision. Tensions among effectiveness, legitimacy, sustainability and responsibility. Duties toward users, communities, teams and institutions. Integrity, conflicts of interest and unintended consequences. Reflection on one's professional path, the country, the final project and responsibility for what one chooses to build.

39. Integrative Workshop VIII. Final Integrative Project III – Completion and Public Defense

Integration and completion of the project developed in Workshops VI and VII. Consolidating research results, a technical system or an entrepreneurial and institutional initiative. Evidence of validity, reproducibility, use, performance, demand or impact, as appropriate. Feasibility, sustainability, risks and consequences. Retrospective review of decisions and their limits. Comprehensive documentation and a proposal for continuation, transfer, further development or closure. Public defense before an external panel of the work, evidence, learning, the student's own contribution and the project's limits.

11. Effective date

This curriculum will take effect upon approval by the Superior Council of the University of Buenos Aires.

12. Equivalences with the Ciclo Básico Común

For academic recognition within the University of Buenos Aires, passing the modules of the Intensive Introductory Course of the Bachelor's in Complex Systems grants the following equivalences with courses of the Ciclo Básico Común (CBC, the university's common first-year cycle):

Passing the THREE (3) modules of the Intensive Introductory Course therefore entails recognition of the SIX (6) Ciclo Básico Común courses listed above.

The equivalences in this section are one-way and apply only from the passed modules of the Intensive Introductory Course to the corresponding Ciclo Básico Común courses.

Consequently, passing one or more of the Ciclo Básico Común courses listed in the table above does not grant full or partial equivalence for the modules of the Intensive Introductory Course, nor does it replace the requirements for passing that Course set for the Bachelor's in Complex Systems.