“Philosophy must not interfere in any way with the actual use of language,
so it can in the end only describe it.
For it cannot justify it either.
It leaves everything as it is.
It also leaves mathematics as it is, and no mathematical discovery can advance it. A ‘leading problem of mathematical logic’ is for us a problem of mathematics like any other.”
Ludwig Wittgenstein
What are "Symbolic Systems"?
Symbolic systems refer to the structures through which information, meaning, and knowledge are represented and processed—whether in human minds, computer systems, social structures, etc. This concept originating at Stanford University, provides a lens for studying interdisciplinary exploration. According to Stanford, the name stands: “Systems that are built with symbols, such as natural language, programming languages, and formal logic; and Systems that work with symbols, including minds and brains, computers, networks, and complex social structures.” At SYINT, we utilise symbolic systems as a scope to explore how symbols construct systems of meaning and reveal structural similarities in seemingly different fields. For instance, behavioural patterns, philosophical arguments, and neural networks all have underlying architectures that can be contrasted. This approach enables us to identify these shared architectures, bridge divides, and uncover connections that might remain hidden.
What about "Intelligence"?
Interaction refers to the dynamic processes through which different entities, systems, or disciplines communicate, influence, and transform one another. It covers multiple dimensions of exchange that shape how information flows and meaning emerges. This can include Cross-disciplinary interaction that explores how methodologies, insights, and perspectives across fields can inform and enrich one another; Social interaction investigates how individuals and groups exchange and co-create meaning, identifying cultural patterns and shared understanding; Human-Computer interaction examines the interfaces and communication between humans and computational systems, revealing how designs affect user experience and cognitive processes; and System interaction, which investigates how components within complex systems affect each other; And more.
What about "Interaction"?
Intelligence covers the various ways in which systems (human, artificial, collective, etc.) process information, adapt to changing circumstances, and generate new knowledge. For instance, technical intelligence includes computational approaches like machine learning that simulate or extend cognitive functions. Human cognition examines how individuals think, learn, reason, and process information, drawing from psychology, neuroscience, and cognitive science. Collective intelligence investigates how groups, communities, and social networks solve problems and generate knowledge. Adaptive systems explore how both natural and artificial systems learn and evolve, developing new capabilities in response to environmental challenges. There are other forms of intelligence, and as time progresses, more forms of intelligence may be identified.
How do they complement each other?
These three elements form an integrated framework. Symbolic systems provide the foundational architecture through which meaning is represented and processed across domains. Yet symbols alone exist as static entities until animated through interaction, the dynamic processes that govern how information flows between systems. These interactions of systems shape and are shaped by various forms of intelligence, which process information, adapt to changing circumstances, and generate new knowledge.
When examined together, these three dimensions reveal patterns and connections that might remain invisible when studied in isolation. For example, to understand contemporary issues like the impact of social media on society, we need to consider the symbolic structures of digital platforms, the interaction patterns between users and technology, and the intelligent systems that process and amplify information. This integrated perspective allows us to address complex questions that span traditional disciplinary boundaries, fostering insights that specialized approaches might miss.
What do I need with SYINT?
To engage with SYINT, you don't need expertise in every field we explore—that would defeat our purpose of bridging disciplines. Instead, what's valuable is curiosity about how different fields can illuminate each other and a willingness to translate concepts across disciplinary boundaries. The practice of conceptual engineering—analysing, clarifying, and refining concepts—serves as a practical methodology that supports this cross-disciplinary exploration. Within SYINT, conceptual engineering involves identifying core concepts within specific fields, translating ideas between different domains, refining concepts to better address particular problems, and creating new conceptual frameworks that bridge disciplinary divides. The most significant real-world challenges exist at the boundaries between traditional disciplines—developing ethical AI, understanding the psychological impact of digital media, addressing climate change—making the ability to translate concepts across disciplines increasingly valuable. When you join SYINT, you're developing a meta-skill of conceptual translation that enhances how you approach your primary field of study while preparing you for a world where the most interesting problems rarely fit neatly into a single academic discipline.
What might we contribute to?
SYINT is positioned to contribute to a range of emerging questions at the intersection of human experience, technology, and knowledge systems. The ethical dimensions of artificial intelligence represent a critical area where our framework can illuminate the relationship between symbolic representations in AI systems, the interaction between these systems and human users, and the nature of machine intelligence compared to human judgment. Digital well-being constitutes another important domain, examining how the symbolic structures of online environments, patterns of human-computer interaction, and algorithmic intelligence shape psychological health and social cohesion. Cognitive enhancement offers rich territory for exploration, investigating how technological tools might extend human cognitive capabilities while considering both benefits and potential risks. Knowledge democratization represents a domain where we can examine how symbolic systems might be designed to make specialized knowledge more accessible across different communities and backgrounds. Environmental modelling presents opportunities to apply our framework to understanding complex ecological systems and developing more effective approaches to sustainability challenges. Cross-cultural communication offers a field where the relationship between different symbolic systems, interaction patterns, and forms of intelligence can be explored to facilitate better understanding across cultural boundaries. Through these and other topics, SYINT can generate insights that contribute to addressing complex challenges while developing students' capacity for interdisciplinary thinking and conceptual translation.