Leaning into the Complexity
The world is moving faster. Changes in technology, business and society play into each other, creating feedback loops that are effectively unpredictable. Information is flowing in all directions 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 all 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, established experts 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 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 talk!
THEORETICAL FOUNDATIONS: a note from the Founder & Director
Applied Emergence synthesizes rigor from six 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 a foundations.
This is not a new interest for me; it’s the culmination of a career driven by curiosity. My first serious foray into this area 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 games. Here, we’re assembly a variety of models and analysis techniques to real-world games in business and society.
Foundation 1. Mechanism Design (Reverse Game Theory)
While classical Game Theory (von Neumann) asks how agents play, Mechanism Design asks what rules might we design so that independent agents naturally arrive at the desired outcome.
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.
Foundation 2. Social Physics
Pioneered by researchers like Alex Pentland (MIT), this discipline treats social networks as physical systems. It validates that idea flow and communication topology are better predictors of group performance than individual intelligence.
My Lens: My doctoral research in liquid crystal dynamics established my baseline for this thinking. In both materials dynamics and organizational function, the behavior of a system is strongly shaped by its underlying rules as well as initial conditions and boundary conditions, often leading to surprising emergent outcomes.
Foundation 4. Complexity Science
From cellular automata to agent-based modeling, this field demonstrates how simple local rules generate complex macro-behaviors. It provides the computational toolkit we use to simulate and test your system in silico.
My Lens: Standard intuition fails at scale. My technical work has frequently relied on computational modeling to reveal the non-intuitive macro-behaviors that emerge from simple interactions. Agent-based modeling tools with computational scale and realistic agent behaviors (ranging from psychometric diversity to agents underpinned by LLMs) can greatest extend our understanding and ability to operate in commercial, economic, and social systems.
Foundation 3. Institutional Economics
Building on the work of Nobel laureates Elinor Ostrom and Douglass North, we view organizations not just as markets, but as sets of "humanly devised constraints" that shape interaction and manage the commons.
My Lens: Leading R&D organizations at 3M and Google X provided a living laboratory for observing these constraints. I found that structural and decision-making architectures often dictated innovation outcomes more powerfully than talent density or strategy.
Foundation 5. Behavioral Economics
The "Rational Actor" model of classical economics is notably fleeting in the real world. Inspired by Daniel Kahneman and Richard Thaler, we will model agents driven by cognitive biases, heuristics, and bounded rationality.
My Lens: Systems are seldom neutral. Every set of rules creates a choice architecture that biases behavior one way or another. We aim to design the rules, incentives, and engagement strategies that make desired system functions such as collaboration or efficient execution the path of least resistance, guiding the system toward health without removing agency.
Foundation 6. Organizational Physics
Research at the Santa Fe Institute, led by Geoffrey West, demonstrated that complex systems—from biological organisms to cities and companies — adhere to universal laws analogous to physics first principles. These are not merely metaphors; they are mathematical realities with demonstrated predictive power regarding growth, efficiency, and mortality. We can treat organizational dynamics and interactions as hard science with predictable scaling laws.
My Lens: As an applied computational and experimental physicist, I apply the rigor and tools applicable for understanding, diagnosing, and problem-solving to address real-world issues.