Png;base64,iVBORw0KGgoAAAANSUhEUgAAB0kAAANiAQMAAAA+BWN0AAAAA1BMVEVHcEyC+tLSAAAAAXRSTlMAQObYZgAAANtJREFUGBntwQENAAAAwiD7p34PBwwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4EobIwABrMk2hQAAAABJRU5ErkJggg==

Human Purpose, Collective Intelligence,
Leadership Development

Month: January 2025

  • Toward a Habermas Machine: Philosophical Grounding and Technical Architecture

    Philosophers from Socrates to Bertrand Russell have underscored that genuine agreement arises not from superficial accord but from reasoned dialogue that harmonizes diverse viewpoints. Jürgen Habermas’s theory of communicative action refines this principle into a vision of discourse aimed at consensus through rational argument. Recently, a paper in Science by Michael Henry Tessler et al. (2024) (“AI can help humans find common ground in democratic deliberation”) echoes this idea by describing a “Habermas Machine”—an AI mediator capable of synthesizing individual opinions and critiques to foster mutual understanding. While their study focuses on social and political issues, the underlying concepts extend readily to organizational contexts and knowledge management.

    In our own effort to realize a Habermas-inspired mediator, we employ an architecture that leverages BigQuery as a data warehouse built on a Data Vault schema, managed and orchestrated with dbt (Data Build Tool). The system ingests communications from platforms such as Slack and Gmail, breaking each message into paragraph-level segments for individual vector embeddings. These embeddings are then stored in BigQuery, forming a semantic layer that augments traditional relational queries with more nuanced linguistic searches. In the below diagram, you can see how messages flow from raw capture to an enriched, queryable knowledge graph.

    Png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJYAQMAAADL0F5mAAAAA1BMVEVHcEyC+tLSAAAAAXRSTlMAQObYZgAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAF9JREFUeNrtwQENAAAAwqD3T+3sARQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANydfAAFYF3K5AAAAAElFTkSuQmCC

    This structural framework, however, only solves part of the puzzle. We then introduce LangGraph agents, enhanced by tooling such as LangSmith, to marry textual and structural data. These agents can retrieve messages based not only on metadata (author, timestamp) but also on thematic or conceptual overlap, enabling them to detect undercurrents of agreement or contradiction in vast message sets. In a second diagram, below, you can see how agent-mediated queries integrate semantic vectors, user roles, and conversation timelines to pinpoint salient insights or latent conflicts that humans might overlook.

    Png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJYAQMAAADL0F5mAAAAA1BMVEVHcEyC+tLSAAAAAXRSTlMAQObYZgAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAF9JREFUeNrtwQENAAAAwqD3T+3sARQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANydfAAFYF3K5AAAAAElFTkSuQmCC

    The philosophical impetus behind this design lies in extending what Habermas posits for face-to-face discourse—an “ideal speech situation”—to distributed, digitally mediated communication. Like the “Habermas Machine” described by Tessler et al., our system provides prompts and syntheses that help participants recognize areas of accord and legitimize points of dissent, rather than imposing a solution from on high. A final diagram, below, depicts a feedback loop, where humans validate or refute AI-suggested statements, gradually converging on well-supported, collectively endorsed conclusions.

    A8mAAAAAElFTkSuQmCC

    Ultimately, these tools do not replace human judgment; they aspire to enhance it. By combining robust data engineering on BigQuery with sophisticated natural-language reasoning via LangGraph agents, we strive to ground the ideal of rational consensus in a practical, scalable system. Inspired by recent research and Habermasian philosophy, we envision AI as a diplomatic catalyst—one that quietly structures and clarifies discourse, guiding us toward common ground without diluting the richness of individual perspectives.

  • TP’s NICER Habermas Machine

    We are developing the NICER Habermas machine ….SO COOL

    Png;base64,iVBORw0KGgoAAAANSUhEUgAAA8AAAAPAAQMAAADAGILYAAAAA1BMVEVHcEyC+tLSAAAAAXRSTlMAQObYZgAAAIdJREFUeNrtwTEBAAAAwqD1T20Hb6AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4DfFzwABa9NYigAAAABJRU5ErkJggg==

    I guess I am somewhat of a negotiation professional and proud to be an Alumnus of the Harvard Strategic Negotiations programme created by James K. (“Jim”) Sebenius, the Gordon Donaldson Professor at Harvard Business School. Following that experience, as a commercial exec in the media world I became reasonably fluent in the process of creating multi-party agreements.

    I transitioned from being a commercial exec to lecturing in Sales and Negotiation at Kingston Business School, Kingston University in London. This is where I met our (genius) Director of Intelligent Systems, data and behavioural scientist, economist, former eBay exec and academic Johannes Castner.

    By way of background, we can probably agree that collectively, humans, bless us, struggle with speedy decision-making. Yes, there is too much data to get up to speed with, conflicting priorities, and the ever-present risk of a person hijacking the agenda to explore their current rabbit hole.

    We have created the basis of a new software system and API, that brings together human purpose, the inclusive genius of Collective Intelligence (CI) and a bunch of bespoke AI agents. Also in creating the code, we are drawing heavily on our experience of deal making – that teaches us people are much more likely to agree at the level of values and interests. Unlike many people, NICER quickly finds out what people care about.

    NICER is an acronym of Nimble, Impartial, Consensus-Engendering Resource! Basically a software product/API that uniquely blends AI agent-driven insights with human expertise, to create faster, more harmonious decision-making. Imagine an AI-powered assistant that sifts through mountains of data, tests hypotheses on human behavior (using oTree experiments), and presents known facts and suggested ideas in real-time. The result? Fewer deadlocks, more clarity, and less time spent debating things that don’t matter. It taps into LLM’s that instantly read organisational paperwork, digest strategic papers, regulatory frameworks, news and social media commentary, plus freely and legitimately available, peer reviewed academic papers in order to feedback in real time.

    Building on some solid behavioural science research, the expectation is the consensus-building software system can work with comms platforms like Slack, to help teams focus on critical, contemporaneous issues and make a contribution. Analysing Collective Intelligence forums we discover that many organisations are leveraging AI-driven CI, to enjoy a boost in KPI performance, innovation speed, and employee engagement.

    We are doing this to contribute to an innovative ecosystem that is currently driven by Innovate UK and UKRI, that’s improving productivity, engendering sustainable growth and ultimately regenerating communities across the UK. Next time you find yourself stuck in a slow decisional process, remember: the future belongs to those who think smarter together.