Leaning into the Complexity
The world is moving faster. Changes in technology, business and society play into each other, creating feedback loops that are exceedingly difficult to parse. Information is flowing in all directions at rates and in ways it never has before. How do we navigate real-world challenges in the fog of this complexity?
The conventional approach is to categorize, pattern-match, and boil it down to simple, elegant strategic clarity. This is valuable and has its place, particularly when the situation is one of underlying simplicity. But while boiling the facts down to memorable narratives makes for compelling pitches and great entertainment, it often falls short in diagnosing failure modes and identifying unexpected paths forward.
In their 2015 book “Superforecasting: the Art and Science of Prediction,” Philip Tetlock and Dan Gardner exposed an unfortunate failure of expertise. In an extensive series of controlled prediction experiments, pundits and established domain experts with fixed models consistently under-performed relative to people with the same information who, rather than leaning on their pre-existing models, dug into the data diligently and curiously, doing the serious work of assessing the situation in its full complexity.
A common challenge for experts, whether academic researchers, business leaders, or TV pundits, is the burden of carrying a point of view. Maintaining a unique Big Idea is absolutely useful for its intended purposes. But a curious problem solver with less of an overriding story or established perspective doesn’t have as much at stake narratively and reputationally, and can be less constrained.
Affinity for the simple explanation and solution is built into our brains. We’re driven to find a clear, elegant narrative for challenges and opportunities of all sizes because it’s a useful shortcut in thinking. Our minds are model-building machines that excel at adopting a viewpoint that’s just sufficient to address the task at hand. Mental efficiency is an evolutionary survival tool, and we are cognitively designed around it.
At Applied Emergence, we intentionally and strategically take the opposite tack. Stephen Hawking commented in 2000 that this new century would be the “century of complexity.” Our antidote to simple models and bold stories is honoring the chaos, finding joy in exploring tangled webs of causality, and listening long and hard with real curiosity rather than jumping to answers. And when appropriate, we use the latest in mind-extending complexity-navigating tools such as AI-enhanced agent-based simulations, network analysis, and information diffusion models to explore game theoretical and emergent effects that happen in even relatively small systems.
We’re here to listen and learn, embrace the complexity and uncertainty, challenge our existing models and mindsets, and build paths forward together. Let’s get to work!
THEORETICAL FOUNDATIONS: a note from the Founder & Director
Applied Emergence synthesizes rigor from four distinct, related disciplines to assess the shape of problems, map causality, and ultimately engineer socio-technically intended outcomes. Each of these is established, scientific, and built on trustable foundations.
This is not a new interest for me personally; it’s the evolution of a career driven by curiosity as a physicist surrounded by brilliant people in social and cognitive science and design fields. My first serious foray into simulation of human dynamics was at Google X, where I formed and led an unpublished project exploring application of machine learning models for predicting team function and outcomes in game environments. Here, we’re assembling and applying a variety of models and analysis techniques to the real-world games of business and society.
Below are a few blocks of established work that have inspired me or that demonstrate the underlying credibility and potential for work of this type.
Foundation 1. Mechanism Design (Reverse Game Theory)
While classical game theory (von Neumann) asks how scenarios will play out in interactions between agents given a set of rules, mechanism design asks what rules might we design so that independent agents naturally arrive at an optimal outcome. Nobel Laureates Eric Maskin and Roger Myerson’s work in designing systems that maximize outcomes for all participants broke new ground in system design theory, applying this work to applications such as auctions and elections. Development of new solutions for real world situations through numerical approaches enables elevation of organizational theory (among other topics) to an engineering discipline
My Lens: The mathematical parallel to physics is elegant. My experience in the inverse design of optical films and photonic structures relies on the same principles: defining a target output (light propagation or social behavior) and computationally solving for the structural rules required to produce it. We are actively developing tools and filing patents in this space.
Foundation 2. Complexity Science
From cellular automata to agent-based modeling, simple local rules generate complex macro-behaviors. It provides the computational toolkit we use to simulate and test your system in silico. Building from theoretical physicist and Nobel laureate Philip W. Anderson’s 1972 anti-reductionist essay “More is Different,” this field embraces how the emergent properties of assembled systems drive key behaviors and phenomena in systems around us.
My Lens: Standard intuition fails at scale. My technical work has frequently relied on computational modeling to reveal non-intuitive macro-behaviors. Agent-based modeling tools with computational scale and realistic agent behaviors (ranging from psychometric diversity to agents underpinned by LLMs) can greatly extend our understanding and our ability to navigate commercial, economic, and social systems.
Foundation 3. Behavioral Economics
The "Rational Actor" model of classical economics is notably fleeting in the real world. Inspired by Nobel Laureates Daniel Kahneman and Richard Thaler, we may model agents driven by cognitive biases, heuristics, and bounded rationality.
My Lens: Just like data has intrinsic biases, organizational systems and networks of people have biases toward outcomes. Every decision in organizational design and team structure represents impact on choice architecture that biases individual and group behavior one way or another. We can map effects and design the rules, incentives, and engagement strategies that make desired system functions — such as collaboration or efficient execution — the path of least resistance. Ultimately, we aspire to guide human systems toward health without removing agency.
Foundation 4. Organizational and Social Physics
Research at the Santa Fe Institute, led by theoretical physicist Geoffrey West, demonstrated that complex systems—from biological organisms to cities and companies — adhere to universal laws analogous to physics first principles. We can treat organizational dynamics and interactions as hard science with predictable scaling laws. Similarly, Alex Pentland’s work on Social Physics at MIT demonstrated that social network properties and patterns of interaction shape the function of organizations, and that these can be tuned to improve outcomes.
My Lens: As an applied computational and experimental physicist, rather than being concerned with the theories of societal function, I’m focused on applying the approaches of physics and simulation tools in diagnosing and addressing real-world issues. Properly framing organizational and ecosystem inefficiencies and malfunctions according to physics principles — including scaling laws and network effects, using initial conditions, boundary conditions, and the operating principles within and between organizations — is a valuable tool for diagnosing sticky real-world challenges.