Science-Based Decision Making

10 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗙𝘂𝘁𝘂𝗿𝗲 𝗦𝗲𝗻𝘀𝗲𝗺𝗮𝗸𝗶𝗻𝗴 Navigating uncertainty isn’t just a challenge—it’s an opportunity for visionary leaders. By leveraging foresight-driven sensemaking, you can anticipate change more effectively, develop highly adaptive and antifragile strategies, and unlock transformative innovations in an early stage. Here are 10 essential, field-proven instruments to enhance your foresight and ability to shape a thriving future for you and your organization: 1️⃣ 𝗛𝗼𝗿𝗶𝘇𝗼𝗻 𝗦𝗰𝗮𝗻𝗻𝗶𝗻𝗴 – Detect early signals of emerging trends, risks, and opportunities to stay ahead of the curve. 2️⃣ 𝗧𝗿𝗲𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 – Identify ongoing trends, their drivers, and potential impacts on industries and societies. 3️⃣ 𝗖𝗿𝗼𝘀𝘀-𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 – Evaluate how different trends, events, or factors influence each other over time. 4️⃣ 𝗪𝗲𝗮𝗸 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 & 𝗪𝗶𝗹𝗱 𝗖𝗮𝗿𝗱𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 – Recognize early indicators of change (weak signals) and prepare for high-impact, unexpected events (wild cards). 5️⃣ 𝗙𝘂𝘁𝘂𝗿𝗲𝘀 𝗪𝗵𝗲𝗲𝗹 – A visual brainstorming tool to map out direct and indirect consequences of a change or event. 6️⃣ 𝗗𝗲𝗹𝗽𝗵𝗶 𝗠𝗲𝘁𝗵𝗼𝗱 – A structured forecasting technique that gathers expert consensus to enhance decision-making. 7️⃣ 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 – Develop multiple plausible future scenarios to prepare for uncertainty and explore strategic options. 8️⃣ 𝗖𝗮𝘂𝘀𝗮𝗹 𝗟𝗮𝘆𝗲𝗿𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗖𝗟𝗔) – A deep analysis framework that uncovers different layers of meaning, systemic causes, and underlying worldviews. 9️⃣ 𝗧𝗵𝗿𝗲𝗲 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 – Helps decision-makers think about present and future simultaneously by categorizing innovation and change into three time-based horizons. 🔟 𝗕𝗮𝗰𝗸𝗰𝗮𝘀𝘁𝗶𝗻𝗴 – Starts with a desirable future vision and works backward to identify necessary steps to achieve it. In an era of constant change and opportunity, these tools help you and your organization move beyond short-term thinking and develop long-term strategic foresight to drive imagination, innovation, and antifragility. 👉 Follow Ewa Lombard, PhD, and Sebastian Baumann for more insights on foresight, visionary leadership, and future-fit decision-making. Press 🔔 to stay updated on upcoming posts, articles, and our peer-reviewed papers on these topics. 👉 Find more info on our 2025 special 𝗙𝗨𝗧𝗨𝗥𝗘 𝗨𝗡𝗙𝗢𝗟𝗗𝗜𝗡𝗚 - exclusive visionary leadership retreats and trainings - at Gravity & Grandeur

Great seeing our paper out in Science! Stefano Carattini, John List and I argue that policy evaluation should be combined with a causal analysis of public support. Starting point of our argument is that policies that are generally considered socially desirable by the scientific community are not always popular among voters, because of a lack of understanding or biased beliefs. Congestion charges and carbon taxes are a case in point. However, recent empirical studies have shown that, in cases like these, experiencing the policy may lead voters to correct their beliefs and increase their support. A credible policy evaluation may further help voters to learn about the policy's effects. Our article describes how credible policy evaluation can be fruitfully combined with a causal analysis of public support. If it becomes more widely documented that opposition to sound policies dissipates when voters experience a policy, then policy-makers may be more inclined to experiment with such policies. Learning when and why public support does not increase after policy implementation would be very important as well. Indeed, this may even lead to a change in the consensus about the policy's desirability, for instance when scientists learn that they overlooked some negative aspects of the policy that voters strongly care about. Read the full article here: https://lnkd.in/ed2EAj9G Science Magazine

Running simulations: base model vs. lookahead model I see people posting on the use of “simulations” for planning inventory policies. If you are using a lookahead model (which is typical for most real-world inventory problems), there are two models where simulation can be used:   1.    The base model, which can be a simulator or the real world. 2.    The lookahead model, which is used in the policy for planning the future to make a decision now. See the figure below - I use the same notational style for both models, but the lookahead model uses tildes on each variables, which also carry two time subscripts: the point in time we are making the decision, and the time period within the lookahead model.   The base model is used to evaluate the policy, and is needed to perform any parameter tuning. The base model can be based on history or a simulation of what you think the future can be.   When simulating inventory policies, special care has to be used because we do not have historical data on market demand – we typically just have sales, which can be “censored” (a topic that has been recognized in the inventory literature for over 60 years). For example, if we run out of product (and there is no back ordering), we lose the sales, which typically means that we do not see (or record) them.   I find it is generally best to run simulations using mathematical models of uncertainty so that we can run many simulations, testing different policies. Stockouts depend on properly simulating the tails of distributions, along with market shifts, price changes and supply chain disruptions. There are, of course, settings where you have no choice but to test your ideas in the field. It is expensive, risky, and slow, but sometimes you just have no choice, especially when you have to capture human behavior.   If your policy requires planning into the future, you really need to be using a stochastic (probabilistic) model of the future which properly captures the tails of distributions. With long lead times, you should also plan for the possibility of significant disruptions, which can mean that you also have to capture the decisions you might make in the future. See chapter 19 of:   https://lnkd.in/dB99tHtM (“tinyurl.com/” with “RLandSO”)   for an in-depth treatment of direct lookahead policies. #supplychain #inventory  Nicolas Vandeput Joannes Vermorel

To create good policy you need responsible foresight, enabling ethical, sustainble, accountable future design. AI now can massively enable human-centered responsible foresight, in helping address uncertainty, assess risks, and set policies for creating better futures. María Pérez Ortiz's new paper "From Prediction to Foresight: The Role of AI in Designing Responsible Futures" describes responsible foresight in policy and the role of computational foresight tools. Notable approaches to using AI in responsible foresight include: 🤝 Participatory Futures for Inclusive Planning. Engaging diverse stakeholders in foresight practices democratizes the future-planning process. AI tools streamline public participation by analyzing preferences, simulating collective decisions, and creating urban plans that reflect community values, fostering equity and resilience. 🧠 Superforecasting for Precision and Insight. Superforecasting uses disciplined reasoning and probabilistic thinking to predict uncertain events. AI-powered assistants improve human forecasting accuracy by 23%, aggregating data and refining predictions through collective intelligence and advanced analytical models. 🌐 World Simulation for Systemic Insights. Advanced modeling frameworks simulate interconnected global systems, enabling policymakers to test "what-if" scenarios. AI accelerates these simulations, providing precise forecasts and dynamic platforms to visualize the long-term consequences of policy decisions across economic, social, and environmental domains. ⚙️ Simulation Intelligence for Decision Optimization. By integrating AI with high-fidelity simulations, simulation intelligence explores complex systems to uncover optimal strategies. This tool assists in crafting effective policies for urban planning, sustainable agriculture, and climate resilience, offering actionable pathways for addressing systemic challenges. 📜 AI-Assisted Narrative Techniques. Large language models contribute to speculative futures by generating detailed "value scenarios" that integrate ethical, technological, and societal considerations. These AI-driven narratives enable policymakers to visualize desirable outcomes and evaluate potential trade-offs. 🔗 Hybrid Intelligence for Enhanced Foresight. Combining human creativity with AI’s computational strengths creates a robust foresight framework. Intuitive interfaces, explainable AI, and participatory design ensure that tools remain transparent and aligned with ethical considerations, empowering policymakers to navigate complex challenges collaboratively. ♻️ Iterative Foresight with Feedback Loops. Continuous monitoring and real-time adaptation enhance foresight processes. AI’s ability to process evolving data and generate actionable insights ensures policies remain responsive, flexible, and aligned with long-term objectives. The power of AI in assisting foresight is just beginning to come to fruition.

A "sampled success metric" is a performance measure or evaluation criterion calculated from a sample or subset of data rather than the entire population. Its calculation often involves higher costs per sample, such as manual review, leading to a trade-off between sample size and metric accuracy/sensitivity. In this tech blog, written by the data science team from Shopify, the discussion revolves around how the team leverages Monte Carlo simulation to understand metric variability under various scenarios to help the team make the right trade-offs. Initially, the team defines simulation metrics to describe the variability of the sampled success metric. For instance, if the actual success metric is decreasing over time, the metric could indicate how many months of sampled success metric would show a decrease, termed as "1-month decreases observed". Then, the team defines the distribution to run the Monte Carlo simulation. Monte Carlo simulation, a computational technique using random sampling to estimate outcomes of complex systems or processes with uncertain inputs, draws samples from a dedicated distribution that matches business needs. Based on past observations, the team’s application follows a Poisson distribution. Next comes the massive simulation phase, where the team runs multiple simulations for one parameter and then changes various parameters to simulate different scenarios. The goal is to quantify how much the sample mean will differ from the underlying population mean given realistic assumptions. The final result provides a clear statistical distribution of how much extra sample size could lead to metrics variability decrease and increased accuracy. This case study demonstrates that Monte Carlo simulation could be a valuable toolkit to add to your decision-making and data science knowledge. #datascience #analytics #metrics #algorithms #simulation #montecarlo #decisionmaking – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/dKnrZzzV 

For centuries, scientific progress was driven by observation. Early astronomers charted the sky, physicians recorded anatomy, and natural philosophers catalogued the world. Then, in the 1600s came a pivotal transformation, an awakening of deep curiosity in a period referred to as the Enlightenment. During this time observation evolved into hypothesis, experimentation, and prediction. Newton’s laws did not only describe falling apples; they enabled humanity to understand and even predict the forces at play. Science shifted from observing the natural world to theory and hypotheses of it, and through that change many of the modern conveniences we enjoy today were born. Business is undergoing a similar evolution. Operational excellence and performance analysis began with observation, measuring outputs, identifying inefficiencies, and standardising processes. Frameworks such as Lean and Six Sigma remain grounded in empirical observation and correlation. They excel at explaining what happens and, to a degree, why. Yet much of this remains retrospective. We monitor, we record, and we improve incrementally. In scientific terms, many organisations remain at the stage of saying, “If I drop this apple, it will fall.” Business cases, budgets, and cash flow forecasts are all forms of modelling. However, they extrapolate from established patterns and are based on the assumption that tomorrow will behave much like today. Digital twins and advanced simulations represent this progression. A digital twin replicates a real-world process or system, ingesting data and enabling changes to be tested virtually. These models are increasingly powered by artificial intelligence, including inference models that learn from vast datasets and forecast complex outcomes with growing accuracy. Looking ahead, the potential of quantum computing promises to accelerate this capability further, making it possible to simulate scenarios of previously unmanageable scale and complexity. As in science experiments, these tools could reveal how a change might ripple through a network before any adjustment is made in reality. Today, when we combine data with predictive analytics and simulation it allows organisations to shift from reactive observation to proactive change. Continuous improvement becomes continuous simulation. Rather than waiting for failure to surface opportunity, leaders can test “what if” scenarios in real time. Just as scientific theory enabled experimentation without incurring the full costs of trial and error, predictive modelling allows decision-makers to explore options, optimise outcomes, and allocate resources more effectively before committing to action. Science advanced when people began to theorise and not merely observe. Business now stands at a similar inflection point. Those who embrace predictive experimentation will not only understand their operations more deeply but, like Newton, begin to shape the very principles that define their success.

The world's most valuable skill is critical thinking. Here are 3 decision-making frameworks that will save you dozens of painful hours trying to learn critical thinking for yourself: 1. Chip and Dan Heath's WRAP Framework The measure of a good decision isn't the outcome you produce, but the process you use to make it. Learning this completely changed the way I thought about decision-making, and the importance I placed on process. According to the Heath Brothers, you can overcome common decision biases like narrow framing, confirmation bias, short-term emotions and over-confidence by using these four steps for every significant choice you make. W - Widen your Options R - Reality Test Your Assumptions A - Attain Distance P - Prepare for the Worst. --- 2. Greg McKeown's Essentialism Framework Hang this up in your room somewhere—and stare at it everyday. Greg McKeown, in his book Essentialism, makes the case that the highest point of frustration occurs when we're trying to do everything, now, because we feel like we should. In order to reach the highest point of contribution, we need to do: The Right Thing, at The Right Time, for The Right Reason. When we focus on these three variables, we don't waste time and energy on activities and decisions that aren't a right-fit.  --- 3. Tim Ferris' Fear-Setting Framework I consider this the gold-standard of strategic risk management and contingency planning. Important decisions will always come with risks, consequences and unforeseen problems. Instead of trying to eliminate the negative and plan for the best, Ferris advises people to complete a pre-mortem that simulates potential responses. By drawing up a three column table with: The worst things that might happen The steps you can take to prevent those The ways you will respond if they do happen You're able to prepare for a more pragmatic future, rather than being thrown off course at the first unexpected obstacle. For more information on fear setting, and some useful downloads, check out Tim's blog here. These three frameworks completely changed the way I thought about decision-making, and the support I was able to offer leaders in developing the skills they needed to keep tricky programmes on track. I hope they're useful for you. #leadership #decisions #NotAnMBA

When belief filters the evidence. The bias that protects its own view. A contributor misses a deadline. One mistake. The label forms: “Not ready.” Past wins lose weight. New input gets read to match the label. Confirmation bias: judgment that looks for support. Favored hires get credit for small wins. Others get flagged for the same behavior. Women marked “collaborative” rarely get tagged “leader.” Neurodivergent talent delivers results and still gets called inconsistent. Early views resist change even when stronger data shows up. Hiring leans on “fit” from limited input. Investors overlook flaws in those already favored. Then the system scales it. Models learn from past decisions. The same errors repeat. Evidence gets filtered. Judgment holds. What to do as an individual: Show results that don’t match the label. “Q3 revenue doubled. Does that change it?” State it once. Let it land. What to shift as a leader: Slow the call. For each yes, ask what you may miss. For each concern, ask what would disprove it. Review decisions without names. What to test in systems: Change the order of review. Require one counterexample. Test identical cases with new framing. Track what changes. Belief shapes what gets seen. Opposing evidence corrects it. Where are you protecting a view instead of testing it?

𝐌𝐲 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐚𝐤𝐢𝐧𝐠 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 🧭 Ever found yourself stuck at a career/decision crossroads, paralyzed by indecision? 🤔 Here's my strategic approach to making choices that transform dilemmas into opportunities - The Decision Making Compass "From Confusion to Clarity" 1️⃣ Gain & Loss Ledger Create 2 columns -> Potential Gains vs. Potential Losses * Be brutally honest and comprehensive * Quantify impact wherever possible (financial, career growth, personal development) 2️⃣ Professional Growth Mapping * Visualize each option's trajectory * Ask yourself: "Where does this path lead me in 1, 3, and 5 years?" * Evaluate skill acquisition, network expansion, and learning opportunities 3️⃣ Alignment Check * Does this choice align with your core professional values? * Assess emotional and intellectual satisfaction, not just monetary benefits * Trust your intuition, but back it with rational analysis 4️⃣ Future Proofing 1) Consider long term impact over short-term comfort 2) Embrace choices that challenge you and push boundaries Remember that growth happens outside our comfort zone! "No decision is permanently irreversible. Every choice is a learning experience that shapes your unique professional journey💪" What's your decision making strategy? Share in the comments below! 👇 #CareerGrowth #ProfessionalDevelopment #StrategicThinking #CareerAdvice

Decisions make or break success.   But making smart, timely decisions isn’t always easy—especially when the stakes are high. Great leaders don’t rely on guesswork. They use proven frameworks to bring clarity to chaos. Here are six powerful tools to sharpen your decision-making: 1. Struggling with unclear roles?   ➟ RAPID Framework   This framework clarifies:   - Who decides?   - Who informs?   - Who delivers?  It ensures accountability at every stage. --- 2. Need structure in your process?   ➟ DACI Framework   Assign clear roles:   - Driver: Guides the process.   - Approver: Makes the call.   - Contributors: Provide key insights.   - Informed: Stay in the loop.  Everyone knows their role, reducing confusion. --- 3. Comparing options?   ➟ Decision Matrix   Score your choices based on impact and criteria.   A visual tool to cut through complexity. --- 4. Facing uncertainty?   ➟ Cynefin Framework   Understand your situation:   - Is it simple or chaotic?   - Clear or complex?  This framework points you to the right approach. --- 5. Prioritizing impact?   ➟ Pareto Principle (80/20 Rule)   Focus on the 20% of actions driving 80% of results.   Cut distractions and maximize efficiency. --- 6. Planning strategically?   ➟ SWOT Analysis   Assess your Strengths, Weaknesses, Opportunities, and Threats.   A classic tool for turning insights into action. --- Why these frameworks matter: They bring clarity to chaos, speed to action, and confidence to your decisions. Remember: Smart decisions aren’t just about speed—they’re about direction.  What’s your favorite decision-making framework? Let’s discuss in the comments. If this helped you, share it with your team.   Follow Jay Mount for more strategies on leadership and decision-making.