Mastering Sensemaking: A Guide to Understanding Complex Environments
TLDR in 1 Sentence
Sensemaking is the skill of turning confusing information into clear understanding, which is essential for navigating complex environments like Ethereum and DAO ecosystems.
TLDR in 1 Paragraph
Sensemaking transforms raw data and experiences into meaningful understanding that guides effective action. Unlike simple problem-solving, sensemaking works in messy, uncertain situations where the rules aren’t clear and patterns emerge over time. Think of it as your mental toolkit for making sense of complexity.
In spaces like Ethereum, where rapid change and uncertainty are constants, strong sensemaking skills help you spot emerging patterns, understand interconnected systems, and make better decisions. Sensemaking involves collecting diverse information, looking for patterns, testing interpretations, and continuously updating your understanding as new information arrives.
TLDR in 1 Page
What Sensemaking Really Is
Sensemaking is fundamentally different from traditional analysis. While analysis breaks down known problems into parts, sensemaking creates understanding from ambiguous situations. It’s like being a detective in a mystery where the clues keep changing.
Why It Matters Now
In complex systems like blockchain networks, traditional planning often fails. The environment changes too quickly. What worked yesterday might not work today. Sensemaking helps you stay oriented and effective even when the ground keeps shifting.
Consider Ethereum’s evolution from a simple smart contract platform to a complex ecosystem supporting DeFi, NFTs, and Layer 2 solutions. No one could have predicted this exact trajectory through traditional planning. Success required continuous sensemaking - reading weak signals, understanding emergent patterns, and adapting strategies in real-time.
The Bigger Picture
Mastery of sensemaking creates a compound advantage. Over one year, someone skilled in sensemaking will make consistently better decisions because they understand their environment more clearly. They’ll spot opportunities earlier, avoid pitfalls that others miss, and build more effective strategies.
In network environments like DAOs, individual sensemaking contributes to collective intelligence. When many people in a network develop strong sensemaking skills, the entire system becomes more adaptive and resilient. This is why sensemaking is a core capability for Gitcoin 3.0’s mission to solve Ethereum’s biggest problems.
How to Sensemake
Understanding What Sensemaking Actually Is
Sensemaking is the process by which we give meaning to ambiguous, contradictory, or confusing information.
Put simply, sensemaking is how you figure out what’s happening when it’s not obvious what’s happening.
Karl Weick, one of the pioneering researchers in this field, described it as “the ongoing retrospective development of plausible images that rationalize what people are doing.”
This differs from problem-solving in important ways. Problem-solving assumes you understand the problem and need to find a solution. Sensemaking assumes you need to first figure out what the problem actually is - or whether there even is a problem.
The Core Sensemaking Process
Effective sensemaking follows a recognizable pattern, though it’s rarely linear. Think of it as a spiral where you cycle through different activities, each time building deeper understanding.
Scanning and Noticing Start by expanding your information intake. Look for weak signals - small changes that might indicate bigger shifts. Read beyond your usual sources. Talk to people outside your normal circles. Pay attention to things that seem odd or unexpected.
In the Ethereum space, this might mean monitoring governance discussions across different protocols, tracking developer sentiment via the Electric Capital Developer Report, noticing shifts in sentiment on social media, or noticing changes in gas usage patterns.
Pattern Recognition Once you’re collecting diverse information, look for connections and recurring themes. What keeps showing up? What seems related even though it might not be obviously connected?
Use frameworks like Dave Snowden’s Cynefin Framework to categorize what you’re seeing. Are you dealing with simple, complicated, complex, or chaotic situations? Each requires different approaches.
Interpretation and Hypothesis Formation Create provisional explanations for the patterns you’re seeing. These are working theories, not final answers. Ask yourself: “If this pattern is real, what might it mean? What could be causing it?”
Generate multiple competing hypotheses. Avoid falling in love with your first explanation.
Testing and Updating Test your interpretations through small, safe experiments. Look for evidence that confirms or contradicts your working theories. Be ready to update your understanding as new information arrives.
Practical Sensemaking Tools
Several concrete tools can enhance your sensemaking abilities.
The “So What?” Test For every piece of information you encounter, ask “So what?” This forces you to move beyond data collection to interpretation. If you can’t answer “so what,” you might not need that information.
Multiple Perspectives Exercise Deliberately seek out viewpoints that contradict your current understanding. If you think something is positive, find smart people who think it’s negative. This helps you escape your own mental traps.
Timeline Mapping Create visual timelines of events, especially for complex situations. Look for patterns in timing, sequences, and relationships between events.
Iteration - First, you scan your environment for signals and information. Second, you look for patterns and connections that weren’t obvious before. Third, you create provisional interpretations of what these patterns might mean. Fourth, you test these interpretations through small actions and adjust based on what you learn. Repeat until success.
Weak Signal Amplification Systematically collect and analyze weak signals - early indicators of change that most people ignore. Keep a journal of unusual observations and review it regularly for patterns.
Sensemaking in Action: The DeFi Summer Example
Consider how sensemaking might have helped during DeFi Summer 2020. Traditional analysis might have focused on individual protocols or tokens. Sensemaking would have looked at the broader pattern: composable financial primitives creating emergent value through unexpected combinations.
Someone practicing good sensemaking might have noticed the early experiments with yield farming, the increasing sophistication of governance tokens, and the growing confidence in smart contract security. They might have connected these dots to see an emerging financial ecosystem, not just individual investment opportunities.
This kind of pattern recognition, interpretation, and testing helps navigate complex environments where traditional analysis falls short.
Why This Matters
Sensemaking and Ethereum
Gitcoin 3.0 focuses on solving Ethereum’s biggest problems - scalability, coordination, and sustainable funding for public goods. These aren’t simple technical problems with known solutions. They’re complex, interconnected challenges that require continuous sensemaking.
We are starting the road to GG24 with what we call “Sensemaking SZN” - a time for us to engage with constituents in Ethereum to figure out what problems exist in Ethereum and finding network-based ways of understanding them or solving solutions for them.
This is in contrast to Gitcoin 2.0, when we tried to Quadratic Funding our way out of every problem.
When you understand the patterns in the design spaces, you can design better interventions…
The Challenge of Ethereum’s Evolution
Ethereum’s development faces constant uncertainty. Layer 2 solutions, consensus mechanism changes, regulatory pressures, and ecosystem growth all interact in unpredictable ways.
Traditional planning assumes you can predict these interactions. Sensemaking assumes you can’t - but you can get better at understanding them as they emerge.
For developers, this means building more adaptive systems. For investors, it means focusing on fundamental capabilities rather than specific predictions. For communities, it means developing collective sensemaking capabilities.
For Gitcoin, it means developing a schelling point where people can practice sensemaking, figure out the biggest problems in Ethereum that we can help with, and pair that with capital + innovative capital allocation technologies targeted at those problems.
Global Implications: Navigating Uncertainty
Beyond blockchain, we live in an era of increasing complexity and uncertainty. Climate change, technological disruption, and social transformation create environments where traditional planning fails.
Sensemaking becomes a core life skill. It helps you stay oriented when external frameworks break down. It helps you spot opportunities others miss. It helps you avoid being paralyzed by uncertainty.
Compounding Value
Imagine developing strong sensemaking skills over the next year. You’d start noticing patterns others miss. You’d make better decisions because you understand your environment more clearly. You’d waste less time on approaches that don’t fit your situation.
In professional contexts, you’d become more valuable because you can navigate uncertainty while others get stuck. In investment contexts, you’d spot trends earlier and avoid obvious pitfalls. In community contexts, you’d help groups make better collective decisions.
The compound effect is significant. Better sensemaking leads to better decisions, which leads to better outcomes, which provides better feedback for future sensemaking. It’s a positive feedback loop that accelerates over time.
Forms of Knowledge
The Knowledge Landscape
Effective sensemaking requires understanding different types of knowledge and how they complement each other. Not all knowledge is the same, and different situations call for different kinds of understanding.
Explicit Knowledge This is formal, documented knowledge - the kind you find in textbooks, whitepapers, and official documentation. In the Ethereum space, this includes protocol specifications, governance proposals, and technical documentation.
Explicit knowledge is essential but insufficient for sensemaking. It tells you the rules but not how they play out in practice.
Tacit Knowledge Tacit knowledge is practical, experiential understanding that’s difficult to articulate. It’s what experienced developers “know” about writing secure smart contracts beyond what’s in the documentation. It’s what community builders understand about managing online discussions.
This knowledge comes from experience and observation. You can’t just read about it - you have to develop it through practice.
Emergent Knowledge In complex systems, new knowledge emerges from the interactions between components. No one designed DeFi’s composability features, but they emerged from how different protocols interacted.
Emergent knowledge requires paying attention to system-level patterns, not just individual components.
Empirical Knowledge This is knowledge gained through observation and experimentation. In blockchain contexts, this includes data analysis, user behavior studies, and the results of protocol experiments.
Empirical knowledge helps ground your sensemaking in reality rather than speculation.
A Priori Knowledge Some knowledge exists independent of experience - logical principles, mathematical relationships, and fundamental constraints. In blockchain contexts, this includes cryptographic principles and economic fundamentals.
A priori knowledge provides stable foundations for reasoning in uncertain environments.
Combining Knowledge Types
Effective sensemaking integrates all these knowledge types. You use explicit knowledge for baseline understanding, tacit knowledge for practical insight, empirical knowledge for testing, a priori knowledge for fundamental constraints, and stay alert for emergent patterns.
Consider understanding a new DeFi protocol. Explicit knowledge tells you how it’s supposed to work. Tacit knowledge from experienced users reveals how it actually works. Empirical knowledge shows usage patterns. A priori knowledge identifies fundamental risks. Emergent knowledge reveals unexpected use cases.
Theories of Change and Why They Matter
A theory of change is your hypothesis about how specific interventions lead to outcomes. It’s your model of cause and effect in your specific context.
Theories of change matter because they make your assumptions explicit. Instead of hoping your actions will work, you can articulate why you think they’ll work. This enables better testing and learning.
In Ethereum’s context, a theory of change might connect funding public goods to ecosystem health to long-term sustainability. Making this theory explicit helps identify assumptions to test and metrics to track.
Rudimentary Example Theories of Change:
- Ethereum Mainstream Adoption: If Ethereum successfully scales L2s, provides intuitive UX, and ensures regulatory clarity, then mainstream users and institutions adopt Ethereum widely, catalyzing a decentralized internet economy.
- Ethereum Localism: If Ethereum communities deploy local tokens and governance systems tailored to specific community needs, then local economies become more resilient, equitable, and participatory, fostering economic independence.
- Open-Source Public Goods Funding: If funding mechanisms such as quadratic funding, retroactive grants, and hypercerts are integrated into widely-used blockchain platforms, then open-source projects receive sustainable, transparent, and meritocratic funding, ensuring continuous innovation and infrastructure growth.
Alternative Perspectives on Sensemaking
Beyond Weick’s foundational work, several other traditions offer valuable insights.
Cynefin Framework (Dave Snowden) This framework categorizes situations into simple, complicated, complex, chaotic, and disorder domains. Each domain requires different approaches to understanding and action.
In simple domains, follow best practices. In complicated domains, analyze and apply good practices. In complex domains, experiment and adapt. In chaotic domains, act quickly to establish stability.
Epistemological Considerations Epistemology - the study of knowledge itself - raises important questions for sensemaking. How do you know what you know? What are the limits of your understanding? How do biases shape your interpretation?
Good sensemaking requires epistemological humility - recognizing the limits and biases in your own knowledge while still being willing to act on imperfect information.
Eastern Approaches Eastern philosophical traditions offer different perspectives on understanding complexity. Concepts like wu wei (effortless action) and systems thinking provide alternatives to Western analytical approaches.
These traditions emphasize acceptance of uncertainty, attention to relationships rather than individual components, and action that works with natural patterns rather than against them.
Complex vs Traditional Environments
Understanding the Distinction
Traditional environments are characterized by predictability, clear cause-and-effect relationships, and stable rules. Complex environments feature uncertainty, emergent properties, and constantly shifting dynamics.
The difference isn’t just academic - it determines which approaches work and which fail.
Characteristics of Traditional Environments
In traditional environments, you can plan effectively because the future resembles the past. Cause and effect are clear and proportional. Small changes lead to small effects, large changes lead to large effects.
Think of manufacturing processes, established bureaucracies, or mature markets with clear rules. These environments reward analysis, planning, and execution of predetermined strategies.
Characteristics of Complex Environments
Complex environments feature several distinctive properties that make them challenging to navigate.
Emergent Properties The whole behaves differently than you’d predict from understanding the parts. Ethereum’s ecosystem exhibits properties that no one designed into the original protocol.
Non-linear Relationships Small changes can have large effects, and large changes might have small effects. A single tweet can crash markets, while major technical improvements might go unnoticed.
Path Dependence History matters. Current options depend on previous choices. Ethereum’s development path constrains current possibilities in ways that wouldn’t be obvious from just looking at current technology.
Multiple Stable States Complex systems often have several possible equilibria. Network effects can lock in suboptimal solutions, while coordination can shift systems to better states.
Why Sensemaking Matters More in Complex Environments
Traditional analytical approaches fail in complex environments because they assume predictability and linear relationships. You can’t analyze your way to understanding emergence or non-linear effects.
Sensemaking works because it assumes uncertainty and focuses on pattern recognition rather than prediction. Instead of trying to forecast the future, you try to understand current dynamics and adapt accordingly.
Ethereum Ecosystem as a Complex Environment
Ethereum’s Ecosystem exemplifies complexity in action. There’s no formal governance structure, yet important decisions get made. Multiple stakeholders with different interests somehow coordinate on protocol upgrades, dapps are deployed that change the state machine of Ethereum, which creates new economies (themselves complex systems).
Understanding this requires sensemaking rather than traditional analysis. You need to pay attention to community narratives, informal leadership patterns, and how technical constraints interact with social dynamics, onchain data, and more.
Consider the transition to Proof of Stake. Traditional analysis might focus on technical specifications and implementation timelines. Sensemaking would also consider community concerns, validator incentives, how the change affects Ethereum’s narrative and positioning, and reflexive relationships between all of the above.
Practical Implications
In complex environments like Ethereum, success requires different approaches:
Experimentation over Planning Instead of trying to predict outcomes, run small experiments and learn from results. Gitcoin’s approach of testing different funding mechanisms exemplifies this approach.
Pattern Recognition over Analysis Pay attention to recurring patterns and dynamics rather than trying to break down problems into component parts.
Adaptation over Optimization Focus on maintaining effectiveness as conditions change rather than optimizing for specific scenarios.
Collective Intelligence over Individual Expertise Complex environments often exceed any individual’s capacity to understand. Successful navigation requires collective sensemaking capabilities.
Build sensemaking mechanisms Now that we’ve got web1, web2, and web3 primitives, what kind of state machines can we build that help the ecosystem sense and respond to its biggest problems?
Raising Network Intelligence
The Collective Dimension of Sensemaking
Individual sensemaking is powerful, but collective sensemaking unlocks even greater potential. When groups and networks develop shared sensemaking capabilities, they can navigate complexity that would overwhelm any individual.
Understanding Network Intelligence
Network intelligence is a system’s collective ability to process information, recognize patterns, and adapt to changing conditions. It emerges from the interactions between individual agents who each have their own sensemaking capabilities.
Think of it as distributed cognition. No single node needs to understand everything, but the network as a whole can process complex information and respond intelligently to changing conditions.
Building Blocks of Collective Sensemaking
Several elements contribute to effective collective sensemaking in networks.
Information Diversity Networks need access to diverse information sources to avoid blind spots and groupthink. This means creating incentives for people to share different perspectives and seek out contrarian viewpoints.
In Ethereum’s ecosystem, this diversity comes from developers, users, validators, researchers, and community members who each see different aspects of the system.
Signal Amplification Mechanisms Important information needs ways to spread through the network quickly. This includes both formal mechanisms like governance forums and informal mechanisms like social media discussions - quadratic funding is an example of a signal amplification mechanism. As is info finance.
Feedback Loops Networks need ways to test interpretations and learn from results. This requires mechanisms for proposing hypotheses, running experiments, and sharing outcomes.
Synthesis Capabilities Individual insights need to be combined into collective understanding. This requires both tools and processes for aggregating distributed intelligence.
Strategies for Enhancing Network Sensemaking
Transparency and Open Information Make information accessible to network participants. This includes not just data, but also the reasoning behind decisions and the assumptions underlying strategies.
Public roadmaps, governance discussions, and impact reports exemplify this approach. Transparency enables distributed sensemaking by giving network participants the information they need to form their own interpretations.
Structured Dialogue Processes Create forums and processes that encourage productive disagreement and collaborative interpretation. This goes beyond simple voting or discussion - it requires designed processes that help groups make sense together.
Examples include prediction markets, structured debates, and facilitated sensemaking sessions that help groups move from individual perspectives to shared understanding.
Decentralized Experimentation Enable network participants to run their own experiments and share results. This creates a distributed learning capability that no centralized system could match.
Ethereum’s Layer 2 ecosystem exemplifies this approach. Different teams experiment with different scaling solutions, and the network learns collectively from these diverse experiments.
Cross-Pollination Mechanisms Create ways for insights from one part of the network to spread to other parts. This includes both formal mechanisms like conferences and informal mechanisms like collaborative projects.
Recognition and Reward Systems Align incentives to encourage high-quality sensemaking contributions. This might include reputation systems, token rewards, or social recognition for people who help the network understand its environment better.
Case Study: Ethereum’s Collective Sensemaking
Ethereum’s development provides a rich example of collective sensemaking in action.
Distributed Problem Solving No single entity controls Ethereum’s direction, yet the ecosystem consistently identifies and addresses important challenges. This happens through distributed sensemaking across research teams, development teams, and community members.
Emergent Coordination Major decisions like the transition to Proof of Stake emerge from collective sensemaking rather than centralized planning. Multiple stakeholders contribute different perspectives, and consensus emerges through ongoing dialogue and experimentation.
Layer 2 Innovation The explosion of Layer 2 solutions represents collective sensemaking about scaling challenges. Different teams explored different approaches, and the ecosystem learned collectively from these experiments.
Practical Applications for Gitcoin 3.0
Several specific strategies could enhance sensemaking capabilities within Gitcoin’s network.
Impact Measurement Networks Create systems for collectively measuring and interpreting the impact of public goods funding. This goes beyond individual metrics to collective understanding of what works and why.
Cross-Ecosystem Learning Facilitate sensemaking across different blockchain ecosystems. What can one L2 learn from another? How do insights (or funding) from one domain apply to others?
Community Intelligence Systems Build tools and processes that help community members contribute to collective sensemaking about ecosystem challenges and opportunities.
Feedback Integration Create better mechanisms for integrating feedback from funding recipients, developers, and users into strategic sensemaking about ecosystem needs and priorities.
The Network Effect of Sensemaking
As more participants in a network develop strong sensemaking skills, the entire system becomes more intelligent and adaptive. This creates a positive feedback loop where better collective understanding leads to better decisions, which creates better outcomes, which improves the environment for further sensemaking.
This is why investing in sensemaking capabilities is particularly powerful in network contexts. The benefits compound across the entire system, creating resilience and adaptability that no centralized system could match.
Conclusion
Sensemaking transforms how we navigate uncertainty and complexity. Rather than hoping to predict the future, we develop the capacity to understand and adapt to whatever emerges.
The core insight is simple but powerful: in complex environments, pattern recognition and adaptive response matter more than analysis and planning. This doesn’t mean abandoning rigor - it means applying rigor to the right problems in the right ways.
For individuals, mastering sensemaking creates a compound advantage. You’ll make better decisions because you understand your environment more clearly. You’ll spot opportunities earlier and avoid pitfalls that others miss. Over time, this builds into a significant competitive advantage.
For networks and communities, collective sensemaking unlocks even greater potential. When groups can make sense together, they can navigate complexity that would overwhelm any individual. This is particularly powerful in decentralized systems where coordination must emerge rather than be imposed.
The practical skills are learnable: expanding information intake, recognizing patterns, forming and testing hypotheses, and updating understanding based on feedback. The frameworks - from Cynefin to theories of change to epistemological humility - provide structure for this learning.
In the context of Ethereum and blockchain development, these capabilities are essential rather than optional. The pace of change and the complexity of interactions make traditional planning approaches insufficient. Success requires continuous sensemaking at both individual and collective levels.
Gitcoin 3.0’s focus on Ethereum’s biggest problems exemplifies this need. Scalability, coordination, and sustainable funding aren’t just technical challenges - they’re complex adaptive challenges that require ongoing sensemaking to navigate effectively.
The invitation is clear: develop your sensemaking capabilities, contribute to collective intelligence, and help build systems that can adapt and thrive in uncertainty.
Join us for sensemaking szn during GG24/Gitcoin 3.0. The future of Ethereum depends on our collective ability to make sense of complexity and adapt accordingly.