Efficient Decision-Making Process

How do you measure systems change? A recent report by the Freedom Fund identifies 9 methods that are particularly well-suited to measuring complex, non-linear systems change — especially in contexts like modern slavery, advocacy, and power shifting. Here’re the leading approaches: 1️⃣ Outcome Harvesting - Start with the change, then trace back to identify contributing interventions. Great for capturing both intended and unintended outcomes. 2️⃣ Most Significant Change (MSC) - Collects stories of change from stakeholders, then collaboratively decides which are most significant. Ideal when change is unpredictable. 3️⃣ Process Tracing - Tests causal pathways between interventions and outcomes. Useful for examining whether theories of change hold up. 4️⃣ Narrative Assessment - Co-creates stories with advocates to unpack the how and why of change, with a focus on decision-making and strategy. 5️⃣ Ripple Effects Mapping (REM)- Participatory mapping of a project’s wider impact. Visualises intended and unintended effects across a system. 6️⃣ Bellwether Method - Uses interviews with influential actors to gauge whether an issue is gaining traction in public discourse or policy. 7️⃣ SenseMaker® - Gathers micro-narratives from diverse voices and lets participants interpret their own stories, blending qualitative and quantitative insights. 8️⃣ Social Network Analysis (SNA) - Maps relationships and influence within a system, highlighting enablers and blockers of change. 9️⃣ General Elimination Methodology - A structured way to rule out weak causal explanations, narrowing down to the most convincing evidence for what caused change. These methods help evaluators move beyond metrics to capture shifts in relationships, power and norms — the essence of systems change. Source of images: https://lnkd.in/eesfmqUM

Measuring what works in conservation Conservation has never lacked ideas. Protected areas, payments for ecosystem services, community management, certification schemes, and public campaigns have all been promoted as responses to biodiversity loss. What has often been missing is reliable knowledge about how well these interventions work, for whom, and under what conditions. A growing body of research argues that answering those questions requires moving beyond counting activities to determine whether outcomes can truly be attributed to conservation actions. Recent commentaries highlight this shift. One warns that scarce funds may be directed toward “well-intentioned but ineffective efforts” without stronger causal evidence. Another argues that biodiversity policy suffers from an “evidence problem,” with many interventions not grounded in robust research. Together, they reflect a field attempting to move from persuasion to proof. Traditional conservation monitoring tracks trends such as forest cover or species abundance. These indicators are useful but do not reveal why change occurred. A forest might remain intact because of protection, or because it lies far from roads & markets. Impact evaluation addresses this uncertainty by asking what would have happened without the intervention (the counterfactual). Because this alternative reality cannot be observed directly, researchers approximate it using comparison groups or statistical methods. Establishing causation is difficult in complex socio-ecological systems. Protected areas, for example, are rarely placed randomly; they are often located where deforestation pressure is already low. Studies that fail to account for this selection bias can overestimate effectiveness. More rigorous approaches frequently produce smaller but more credible estimates of impact. To address these challenges, conservationists increasingly borrow methods from economics & public health. Randomized controlled trials offer the strongest evidence but are often impractical or unethical. Quasi-experimental techniques attempt to construct credible counterfactuals when experiments are not feasible. No single method suits every context, and evaluation needs evolve as projects mature. Evidence gaps remain substantial. Many strategies have been studied unevenly across regions, and practitioners often lack the resources to interpret complex analyses. Institutional incentives can also discourage rigorous evaluation, as organizations may feel pressure to demonstrate success rather than uncertainty. Despite these obstacles, the emerging consensus is pragmatic. Not every project requires a randomized trial, but most benefit from a clear theory of change & systematic learning. Biodiversity loss continues at a pace that leaves little room for ineffective interventions. Determining what works will not solve the crisis on its own, but without that knowledge, even well-funded efforts risk missing their mark.

Can we use #LCA to measure a product system's impact on #biodiversity ❓ The answer is yes❗ - How reliable are these calculations? Well, that is up for discussion. The impact on biodiversity should always be measured in situ by surveying the species richness of and ecosystem and in combination with other techniques usually including local communities' knowledge. - Why do I think so? Because ecosystems are essentially unique everywhere we look, the impact of a substance emission or material extraction from nature (elementary flows) varies from region to region. It is different to perform a given activity in an urban area than in a rainforest. However, in the last decade, new Life Cycle Impact Assessment methods have been developed to account for regional differences in the impact on biodiversity. They typically focus on assessing the impacts of #landuse and land-use change, as these are among the most significant drivers of biodiversity loss. They may quantify impacts in terms of potentially disappeared fractions of species (PDF) over a certain area and time (usually m2/year) or use other metrics to estimate the change in species richness or ecosystem quality. Some of the methods that include approaches to assess biodiversity impacts are: ➖ ReCiPe: a comprehensive LCIA method that includes a model for assessing land use impacts on biodiversity through the PDF metric. It aims to quantify species loss over a certain area and time due to land use. ➖ IMPACT World+Endpoint: This method includes an attempt to integrate biodiversity impacts through several impact categories such as the PDF from freshwater acidification, damage to ecosystem quality from changes in the soil pH, marine acidification, ecotoxicity, land transformation and occupation, water pollution, and water availability. It is one of the most complete. ➖ USEtox: focused on toxicological impacts, includes considerations for ecotoxicity, which indirectly affects biodiversity by assessing the potential toxic impacts on aquatic and terrestrial species. ➖ Land use biodiversity (Chaudhary et al., 2015): recommended by the UNEP-SETAC Life Cycle Initiative: "The indicator represents regional species loss taking into account the effect of land occupation displacing entirely or reducing the species that would otherwise exist on that land, the relative abundance of those species within the ecoregion, and the overall global threat level for the affected species." I love this method because includes regional factors. ➖ Global Biodiversity Score (GBS): not a traditional LCIA method, GBS is a tool developed to help companies assess their impact on biodiversity. Using a common metric, it translates pressures from organizational activities into impacts on biodiversity. We need to think way beyond #carbonfootprint to aim for a #sustainable world. Biodiversity loss is that issue that although highly interlinked with #climatechange, is the actual major environmental issue we face.

New AI Research on World Models 📢 Introducing AdaJEPA, an adaptive world model that plans, acts, and adapts in a closed loop. Every action leads to a new observation, and every transition refines the latent representation and prediction. Latent world models enable agents to plan by predicting the future in a compact latent space. However, they are usually kept frozen after training. Inaccurate predictions, especially severe under test distribution shift, can make MPC optimize actions for the wrong imagined future, thus hindering planning. AdaJEPA addresses this by adapting the world model during deployment. Given a pretrained world model, at every MPC step, execute the first action, collect the observation, update WM to minimize latent prediction error, then replan with the updated model. This forms a plan-execute-adapt-replan loop that continually improves prediction. With as few as one gradient step per replan, AdaJEPA consistently yields better planning in both in-distribution and ood test environments, including unseen object shapes, visual corruptions, dynamics shifts, and new maze layouts. We also find that adaptation is highly data-efficient: in low-data regimes, AdaJEPA outperforms frozen models trained with substantially more data. For more details, please check our 📑Paper: https://lnkd.in/gqA5pfTc 🖥️Website: https://lnkd.in/gmmGTbgH Thanks to my collaborator Oumayma Bounou and my advisors Prof.Yann LeCun and Prof.Mengye Ren!

In the world of leadership, making tough calls is inevitable, especially in times of uncertainty. Effective decision-making is a critical skill that can make or break a leader's success. Here are some strategies that have proven effective in my journey and can help you navigate the most challenging decisions: 1. Adopt a Robust Framework - OODA Loop (Observe, Orient, Decide, Act): This framework encourages rapid assessment and adaptation to changing conditions. It helps leaders stay agile and responsive. - Decision Matrix: Evaluate options based on criteria such as impact, feasibility, and alignment with organizational goals. This structured approach ensures comprehensive evaluation. 2. Balance Data and Intuition - Data-Driven Insights: Leverage data analytics to inform your decisions. However, don’t underestimate the power of your intuition, honed through experience and deep understanding of your field. - Scenario Analysis: Develop and analyze multiple scenarios to prepare for various potential outcomes. This helps in making informed decisions even in uncertain environments. 3. Engage a Diverse Advisory Group - Diverse Perspectives: Surround yourself with advisors from different backgrounds and expertise. Their varied viewpoints can uncover blind spots and offer innovative solutions. - Collaborative Decision-Making: Involve your team in the decision-making process. Collaboration fosters buy-in and leverages collective intelligence. 4. Maintain Flexibility and Agility - Iterative Approach: Break down decisions into smaller, manageable parts. This allows for adjustments based on feedback and evolving circumstances. - Pivot When Necessary: Be prepared to pivot if the situation demands it. Flexibility is crucial in navigating the complexities of the business landscape. 5. Focus on Long-Term Vision - Alignment with Vision: Ensure that your decisions align with the long-term vision and strategic goals of your organization. This keeps you on the right track even when immediate circumstances are challenging. - Sustainable Solutions: Aim for decisions that provide long-term value rather than quick fixes. 6. Reflect and Learn - Post-Mortem Analysis: After major decisions, conduct a thorough analysis to understand what worked and what didn’t. This continuous learning loop improves future decision-making. - Celebrate Successes and Learn from Failures: Acknowledge and celebrate your successes, but also embrace failures as learning opportunities. What strategies have you found effective in making tough decisions? #Leadership #DecisionMaking #StrategicThinking #ValueCreation #Entrepreneurship #PrivateEquity #VentureCapital #ConstructiveRebels

I'd like to share with you a powerful method that's been instrumental in our journey towards making more nuanced and balanced decisions. The Six Hat Solution, developed by Edward de Bono, is a powerful tool for teams and leaders. It's designed to help people explore different perspectives towards a complex situation or challenge, making our decision-making process more structured and comprehensive. 1. Emotional Viewpoint: Reflecting on our emotions offers initial insights. How does this situation make us feel? Personally, the prospect of our upcoming project invokes a mix of excitement and apprehension. Acknowledging our feelings can highlight potential concerns or areas of strong motivation. 2. Factual Analysis: Grounding our discussion in facts ensures a solid foundation. What are the undeniable truths of our current situation? With our project, the realities include our deadlines, budget constraints, and the resources at our disposal. These facts help clarify the scope of our challenge. 3. Optimistic Outlook: Focusing on the positives, we identify which aspects are most likely to succeed. In our scenario, the creativity and resilience of our team stand out as invaluable assets. This positivity is crucial for maintaining momentum. 4. Critical Perspective: Conversely, acknowledging what might not work allows us to anticipate and address potential issues. For us, the constraints of time and the untested nature of some technologies are concerns that need strategic planning. 5. Creative Exploration: By thinking creatively, we open the door to innovative solutions. Could adjusting our approach or incorporating new methodologies enhance our outcome? This phase pushes us beyond our initial assumptions. 6. Synthesised Solution: Finally, integrating all perspectives, we determine the most viable path forward. A phased project implementation, leveraging both proven and new technologies in stages, appears to be our best strategy. What complex decisions are you facing that could benefit from this multi-perspective approach? #leadership #mindset #culture #growth #success #problemsolving

Planning into an uncertain future VII An alternative approach to making decisions that model the impact of a decision now on the future (that is, a form of “lookahead policy”) is to approximate the value of being in the state that a decision x_t now leads you to (and this downstream state may depend on information that does not arrive until after you make decision x_t).   There are four strategies we might use to create this approximation (see equations below):   1)   If the state variable S_t is discrete, and there are not too many states (hint – this almost never happens) we can compute the value V_{t+1}(S_{t+1}) of landing in state S_{t+1}. 2)   Since we almost never have a small number of discrete states, we can use machine learning to approximate the value of being in a state. This opens up a range of algorithmic strategies studied under the umbrella of “approximate dynamic programming” and “reinforcement learning.” However, there is still an expectation within the “max” operator which can cause problems (typically expectations can never be computed exactly). 3)   We can use an idea called the “post-decision state variable” which is the state after we make a decision, but before new information has arrived. This means that the term within the max operator is deterministic, and this opens the door to handling decisions that are vectors. 4)   Computer scientists stumbled into this strategy using an idea they call “Q-learning” where Q(s,x) is the value of being in state s and making decision x. This is based on an old idea called “cue learning” introduced in the field of psychology, illustrated by the experiments involving Pavlov’s dog.    This strategy has attracted a tremendous amount of attention, but practical applications tend to be limited to problems where it is possible to estimate reasonable approximations of the value function. This depends on exploiting problem structure or having access to extremely large numbers of estimates of the value function.   Approximating value functions using neural networks (more recently deep neural networks) is attracting considerable attention, although it is not clear how much of this work is making its way into production. I have been successful using equation (3) for complex resource allocation problems where I can exploit structure such as concavity (when maximizing) – this would never be possible with a neural network.   This material is so rich that it requires five chapters (chapters 14-18) in   https://lnkd.in/dB99tHtM (“tinyurl.com/” with “RlandSO”).  

𝟱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Decision-making frameworks provide leaders with structured approaches to tackle complex problems, improve team alignment, and drive better outcomes. By using these tools, leaders can enhance their decision-making process, save time, and increase the likelihood of making successful choices. Here are 5 powerful frameworks every leader should know: 𝗧𝗵𝗲 𝗖𝘆𝗻𝗲𝗳𝗶𝗻 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 ↳ Description: Helps leaders identify the context of a situation (simple, complicated, complex, or chaotic) and choose appropriate actions. ↳ Used for: Adapting leadership style and decision-making approach based on the nature of the problem. 𝗧𝗵𝗲 𝗚𝗼𝗹𝗱𝗲𝗻 𝗖𝗶𝗿𝗰𝗹𝗲 ↳ Description: Focuses on the "Why," "How," and "What" of decision-making, emphasising the importance of purpose. ↳ Used for: Aligning decisions with core values and organisational mission. 𝗖𝗦𝗗 𝗠𝗮𝘁𝗿𝗶𝘅 ↳ Description: Organises information into Certainties, Suppositions, and Doubts. ↳ Used for: Clarifying knowledge gaps and guiding further investigation before making decisions. 𝗥𝗜𝗖𝗘/𝗜𝗖𝗘 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 ↳ Description: Prioritises options based on Reach, Impact, Confidence, and Ease (or Effort). ↳ Used for: Objectively evaluating and ranking multiple options or initiatives. 𝗘𝗶𝘀𝗲𝗻𝗵𝗼𝘄𝗲𝗿 𝗠𝗮𝘁𝗿𝗶𝘅 ↳ Description: Categorises decisions based on importance and urgency (or impact and reversibility). ↳ Used for: Prioritising tasks and allocating appropriate time and resources to decisions. By incorporating these frameworks into your leadership toolkit, you can enhance your decision-making process, foster better team collaboration, and drive more successful outcomes for your organisation. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝘆𝗼𝘂: Which of these decision-making frameworks resonates most with your leadership style, and why? Share your thoughts in the comments! #LeadershipSkills #DecisionMaking #BusinessStrategy

Whiteboard Wednesday is back after a month of highlighting a customer story every day. Today I want to talk about goal setting and a counterintuitive technique that's helped us achieve outcomes here at FERMÀT that we once thought was impossible. Traditional goal setting fails because it relies on historical trends. Most teams look at their improvement rate from last quarter, then aim to do slightly better—essentially saying "if I was here before and I'm here now, I'll try to get a bit further next quarter." Instead, I challenge my team with this powerful alternative approach: 1. Define the maximum possible Ban historical data from goal-setting discussions. Instead, ask: "What's the theoretical ceiling for this metric given the physics and truths of our business?" 2. Quantify the reality gap Once you've established your theoretical ceiling, examine your current position. This gap reveals exactly what must change to achieve breakthrough results. 3. Challenge core assumptions This forces a crucial conversation: "What's the difference between our business fundamentals and historical outcomes that makes this goal seem unattainable?" When you work backward from theoretical maximums rather than forward from historical trends, you discover entirely new actions required to achieve extraordinary results. This approach works across any business type—whether you're increasing product development velocity or scaling creative testing. The principle remains: determine what's maximally possible given your business fundamentals, then work backward to identify the necessary transformations. What assumptions about your business trajectory could you challenge using this method?

Agents get the hype and attention Workflows quietly get the work done This is why so many jump to Agents, even when not needed Sonali and I run our entire operating system, content system and everyday work with agents and workflows interlinked And, doing this has taught us exactly when to use each First, the definitions: An agent makes decisions on its own Think of it like hiring someone. You give them a goal, and they figure out the steps. Example: "Monitor my inbox and flag urgent client emails." The agent reads each email, decides what's urgent based on criteria, then acts. A workflow follows fixed steps. Think of it like a recipe. Step 1 triggers Step 2 triggers Step 3. No decisions. Example: "When someone fills this form, add them to Airtable, send welcome email, notify team in Slack." Now, here's the decision rubric: When to Build a Workflow Use workflows when you can map the entire process upfront. ↳ The steps don't change ↳ The inputs are predictable ↳ You want it to run exactly the same way every time Example: Lead capture from website form → Webhook catches form submission → Add to CRM → Send email sequence → Notify sales team That's it. Same 4 steps. Every time. When to Build an Agent Use agents when the next step depends on what just happened. ↳ The task requires judgment calls ↳ The inputs vary widely ↳ You need it to adapt based on what it finds Example: Research assistant for weekly newsletter → Reads 20+ sources → Decides which insights are relevant to your focus areas → Summarizes only what matters → Flags trends across multiple pieces The agent makes 50+ micro-decisions you can't script upfront. The Repeatability Test Ask yourself: Can I write down every single step before I start? → Yes = Workflow → No = Agent The Decision Framework: ↳ Workflows for predictable repetition. ↳ Agents for adaptive decision-making. ↳ Both together for complex systems. Start with workflows. Add agents only when you hit the limits of scripting. --------- I am Priyadeep Sinha and I help you go from AI Anxiety to AI Expertise one strategy at a time Every week, I share one complete AI workflow system for leaders, consultants and knowledge workers in my newsletter Work in Beta: https://lnkd.in/gPqYEzaJ