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

Human forecasters augmented by GenAI improve performance by 23% and vastly outperform AI-only predictions. Fascinating new research has uncovered important lessons, not just on Humans + AI forecasting, but more generally AI-augmented thinking. 🔮Human forecasters provided an LLM with a 'Superforecaster' prompt substantially improved their prediction performance. 📊In contrast to studies in other domains, the improvement was consistent across more and less skilled forecasters. 🔄Even the use of biased models improves performance to a similar degree, showing that the value was in providing additional perspectives to be assessed by human judgment. 💬Back-and-forth interaction is critical to value creation. Simple Humans + AI thinking processes such as incorporating predictions is of limited use. Forecasters using the models through their thinking process is high value. 🌈Prediction diversity is not degraded by use fo LLMs, with users not letting the models homogenize their thinking. 🚀Forecasting is an excellent use case and example for AI-augmented thinking. High-level human decision-making is highly complex and cannot be delegated to machines, but LLMs, used well, can substantially improve outcomes. The 'Superforecaster' prompt used in the study and a link to the pre-print paper are in the post. #foresight #forecasting #humansplusai #augmentedintelligence

Tough pill to swallow as scientist at an early-stage biotech startup: If you don’t make the science work, on time & on budget, the company will die. Here’s a tried & tested framework for dealing with that 👇 Science at early-stage startups is 🧪 Fast-moving 🧪 Ever-changing 🧪 And first and foremost……outcomes-oriented! If you’ve spent your entire career to date in academia, this may feel unsettling at first. Here’s a framework for navigating outcomes-oriented science: 1️⃣ Zoom out. Get clear on the scientific & business outcome the startup needs to get to profitability. Focus on identifying unnecessary assumptions are constraining you - even if it’s an assumption your manager or CEO made! Example: You need a cell-line with particular characteristics to produce antibodies, which you will sell. Assumptions: 🧪We should make this cell line in house (should we make an off-the-shelf purchase instead?) 🧪The antibodies should be produced via cell line (is another system possible?) 2️⃣ Break the problem into its scientific/business parts. Example: What needs to be true about this cell line? It needs to grow quickly, cheaply, scale in some way, and have an optimized ability to produce antibodies. First principles thinking is key here! Biologists can take a lot from the engineering playbook. 3️⃣ Parallelize a few strategies to achieve this outcome. Consider: How can you ensure these strategies fundamentally de-risk each other? How can you try to solve the problem from multiple angles such at least one might yield the necessary outcome on time? Example strategies to parallelize: 🧪Purchase several cell lines which produce antibodies well. 🧪Chose 3 x potential in-house cell lines which are derived from very different sources. Optimize for reduced costs, quicker doubling times, and scale. 🧪Throw a small amount of resources at a long-shot technique which uses a microbial system to produce antibodies 4️⃣ Monitor progress regularly and cull projects as needed. Example: After 1 month, the microbial system is yielding surprisingly good results. 24 hours later, all cell line work is de-prioritized and the system starts again, zoomed in on microbials _________ Personally, I think that the startup model of science is exhilarating - it gets pretty addicting to see how much tangible impact you can make in a matter of months, rather than years!

🚀 #TeachMeTuesday: “Failure is an option (2024)” — Why SpaceX out-innovates traditional aerospace Today I pick up a few papers assessing the overall innovation approach and success of SpaceX, and to generate lessons for overall innovation strategy at the firm and country-level. In reality, SpaceX is operating more like a software company than an old-school aerospace giant: → Fast test loops → Learning-driven innovation → Governance that enables failure and iteration The papers show that its innovation system is built on governance mechanisms that embrace failure. For those aiming to accelerate progress in critical technologies — from advanced manufacturing to climate tech to AI — understanding this model might be key. How does SpaceX deliver over 100 launches a year, while cutting costs and iterating faster than state programs? 👉 A great new paper by Vittori et al. (2024) — "Failure is an option: How failure can lead to disruptive innovations"https://lnkd.in/eE8f2hJR — dives deep into how SpaceX systematically designs failure into its governance: → High test cadence → Failure-tolerant engineering culture → Management insulation from public/political backlash → Reuse and rapid iteration 👉 In parallel, Ansar & Flyvbjerg (2022) — "How to Solve Big Problems: Bespoke Versus Platform Strategies" https://lnkd.in/e9k3XjmH show that SpaceX’s platform-based approach (versus NASA’s bespoke project model) delivers: ✔️ 10x cost savings ✔️ 2x faster development ✔️ Lower systemic risk Some interesting facts that are more recent. 📊 SpaceX 2024–25: The governance-driven innovation system in numbers: Launch cadence: 🛰️ 134 Falcon 9 launches in 2024 — more than 50% of global orbital launches Reuse rate: 🔁 ~80% booster reuse (some boosters with 25+ flights) → AINvest 💰 ~$62 million per Falcon 9 launch — nearly 20x cheaper per kg than the Space Shuttle 🌐 Ecosystem feedback & platform thinking Cai et al. (2024) — "SpaceX’s Network Effects and Innovation Strategy Analysis" — further show how SpaceX’s ecosystem works https://lnkd.in/egh94Bmd Starlink → feeds launch revenue More launches → improve learning → funds Starship A true commercial + technological feedback loop 🚀 Prof. Bent Flyvbjerg SpaceX Elon Musk, Tesla and SpaceX News by Newslines Claire Jolly Marit Undseth dominique guellec Mattia Olivari

Understanding Risk & Audit Assessment Framework — The Backbone of Strong Governance In today’s dynamic business environment, managing risk isn’t optional — it’s strategic. A Risk & Audit Assessment Framework provides organizations with a structured approach to proactively identify, assess, and mitigate risks while ensuring compliance and operational integrity. Here’s how it works 👇 🔍 1. Risk Identification The process begins with identifying potential risks across financial, operational, strategic, and compliance domains. 📊 2. Risk Analysis & Prioritization Each risk is evaluated based on its likelihood and impact, helping organizations focus on what truly matters. 🛡 3. Control Design & Implementation Robust controls are designed to reduce risk exposure — not just for compliance, but for real risk reduction. 🧾 4. Internal Audit & Validation Audits play a critical role in assessing whether controls are: - Effective - Consistently applied - Aligned with internal policies and external regulations 📈 5. Continuous Monitoring & Reporting Risk management is not a one-time activity. Continuous tracking, reporting, and improvement ensure better decision-making and stronger governance. 💡 Why It Matters A well-defined Risk & Audit Framework: ✔️ Strengthens accountability ✔️ Enhances transparency ✔️ Supports better strategic decisions ✔️ Builds organizational resilience In short, it transforms risk management from a reactive function → to a proactive strategic advantage. 🚀 Final Thought Organizations that embed risk awareness into their culture don’t just avoid failures — they position themselves to scale sustainably and confidently. #RiskManagement #InternalAudit #Governance #Compliance #BusinessIntelligence #DataDriven #EnterpriseRisk #AuditFramework #RiskAssessment #CorporateGovernance #Analytics #DecisionMaking #BusinessStrategy #OperationalExcellence #Leadership #Finance #Controls #GRC #ContinuousImprovement

💡 Stop Guessing: The Right Risk Assessment Drives Your Strategy Choosing the right type of Risk Assessment is not a detail—it's a critical strategic decision. Too often, organizations use a one-size-fits-all approach and end up misallocating resources or missing key threats. The key difference often lies in the data. Qualitative Risk Assessment uses expert judgment and descriptive, non-numeric scales (like High/Medium/Low) to rate severity and likelihood. This helps small teams prioritize quick fixes with a simple heat map. For a data-driven approach, Quantitative Risk Assessment is essential. It uses numerical values (P, %, frequency) to evaluate risk and forecast potential losses or calculate the ROI on controls. A middle ground is the Semi-Quantitative method, which assigns numeric scores (like 1-5 or 1-10) to impact and likelihood, offering more structure than a purely qualitative approach. Risk isn't static. In evolving situations, a Dynamic Risk Assessment is an on-the-spot, real-time evaluation performed when risks shift rapidly or new ones emerge unexpectedly. Furthermore, a Continuous Risk Assessment is a proactive, ongoing process where risks are constantly monitored and adjusted based on new information or threats. Finally, for operational precision, you must choose between: Generic Risk Assessment: A general evaluation covering common hazards across similar tasks or environments. Use this for standardized operations. Site-Specific Risk Assessment: A focused evaluation of risks unique to a particular location, event, or project setup, considering the environment and layout. Choosing based on your environment, data availability, and industry needs is the key to making stronger decisions. #RiskManagement #CyberSecurity #BusinessStrategy #RiskAssessment #DecisionMaking #Security

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐈𝐓 𝐑𝐢𝐬𝐤 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 Key Components of IT Risk Management 1. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭 🔹 Understanding the internal and external environment is foundational for successful risk management. 🔹 This phase defines the organization's objectives, identifies key stakeholders, and evaluates regulatory or compliance requirements that shape risk-related decisions. 🔹 A clear context ensures all subsequent risk management steps are relevant and aligned with organizational priorities. 2. 𝐑𝐢𝐬𝐤 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 Risk assessment is subdivided into several crucial phases: Risk Identification: 🔹 Pinpointing potential threats—such as cyberattacks, hardware failures, or regulatory breaches—that could disrupt IT services, processes, or systems. 🔹 Risk Analysis: Assessing the nature of these risks by analyzing vulnerabilities (e.g., outdated software) and threats (e.g., hackers) to gauge the severity and types of potential impact. 🔹 Risk Estimation: Evaluating each risk’s likelihood and potential impact, typically using quantitative or qualitative methods, to rank and prioritize risks for management focus. 3. 𝐑𝐢𝐬𝐤 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 🔹 Comparison of estimated risks against predefined criteria, such as risk appetite or tolerance levels. 🔹 Determines which risks require action and which can be accepted without intervention. 🔹 Facilitates informed decision-making on where to allocate resources for maximum protection. 4. 𝐑𝐢𝐬𝐤 𝐓𝐫𝐞𝐚𝐭𝐦𝐞𝐧𝐭 Organisations can manage risks using one or more treatment strategies: 🔹 Reduction: Implementing controls or safeguards (e.g., firewalls, security policies) to minimize risk likelihood or impact. 🔹 Avoidance: Altering plans or ceasing activities to entirely bypass certain risks. 🔹 Retention: Accepting a risk when the potential benefits outweigh possible downsides; suitable for low-level risks. 🔹 Transfer: Shifting the risk to a third party, commonly through insurance or contractual arrangements. 5. 𝐑𝐢𝐬𝐤 𝐀𝐜𝐜𝐞𝐩𝐭𝐚𝐧𝐜𝐞 🔹 Organisations formally acknowledge and accept certain risks after due consideration. 🔹 Acceptance reflects the organization’s risk appetite and ensures decision-makers are aware of and prepared for potential consequences. 6. 𝐑𝐢𝐬𝐤 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐑𝐞𝐯𝐢𝐞𝐰 🔹 Ongoing surveillance of the risk environment and the effectiveness of risk management measures. 🔹 Regular reviews help adapt strategies to new threats, changes in technology, or shifts in organizational goals. Maintains an agile and current risk posture. 7. 𝐑𝐢𝐬𝐤 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐭𝐢𝐨𝐧 🔹 Transparent dialogue with stakeholders about identified risks, responses, and rationales behind risk management choices. 🔹 Fosters trust, ensures shared understanding, and supports collaborative risk management efforts throughout the organization. #technology #learning #cybersecurity #ciso

New research with the brilliant Likun Cao, out just today in Technological Forecasting & Social Change: For decades, innovation scholars have debated whether deep technological search (intensive work within a domain) or broad search (combining distant knowledge) drives greater technological impact for the companies that pursue them. Studies keep finding conflicting results. We think we've identified why: they were measuring impact at different time horizons. Using machine learning to map 4.9 million U.S. patents in hyperbolic space, we tracked how citations accumulate over 20 years. The pattern is striking: Deep search drives higher short-term impact—specialized communities recognize and adopt the work quickly. But returns diminish as innovations become "locked in" with limited diffusion potential. Broad search faces initial resistance—category-spanning work is harder to evaluate. But it reaches wider audiences over time and achieves greater long-term impact. The "foundational" and "tension" views of innovation aren't contradictory. They capture different phases of the same process. For R&D strategy: individual inventors and resource-constrained firms may benefit from depth's faster feedback. Larger organizations can alternate—using deep work to build reliable components that later fuel broader exploration. And successful organizations do! Paper: https://lnkd.in/gSFzJRyG

⚠️ What is Risk Assessment? ⚠️ Risk Assessment is the process of identifying hazards, evaluating the risks, and taking appropriate actions to minimize or eliminate potential harm to people, assets, or operations. It helps answer: What could go wrong? 🔥💥⚡ How likely is it to happen? 📊 What would be the impact? 🚑🏢 What controls are in place or needed? 🛡️ 🛠️ Steps to Create a Risk Assessment 🧩 Step 1: Identify Hazards 🔌 Electrical faults 🔥 Fire sources (kitchens, flammable storage) 🧱 Falling objects (scaffoldings, ceiling tiles) 🚿 Water leaks/slips 🦺 Unsafe work practices by staff or contractors 📋 Step 2: Determine Who Might Be Harmed 👷♂️ Staff & Technicians 🧑💼 Tenants or Visitors 🚚 Contractors or Vendors 📉 Step 3: Evaluate the Risks Assess the likelihood and severity: Likelihood (Low/Medium/High) Severity (Minor/Moderate/Major) Use a Risk Matrix to prioritize actions. 🛡️ Step 4: Control Measures Apply the hierarchy of control: 🚫 Eliminate hazard (e.g., remove faulty equipment) 🔁 Substitute with safer alternative 🔐 Engineering controls (guards, barriers) 📋 Admin controls (SOPs, signage) 🦺 PPE (helmets, gloves, goggles) 📝 Step 5: Record & Review Document all risks, controls, and responsibilities Review regularly or when: New equipment is installed A workplace incident occurs Layout or operations change 🏗️ How to Implement Risk Assessment On-Site (As an FM) ✅ 1. Conduct Site Walkthroughs Regular inspections to spot new hazards Involve technical team and safety officer ✅ 2. Create a Risk Register Maintain a centralized record of all risks, controls, and status Use Excel, CAFM, or safety management software ✅ 3. Assign Responsibility Clearly state who is responsible for managing each risk Train technicians & contractors on safety protocols ✅ 4. Share the Assessment Conduct toolbox talks and induction trainings Place safety signage and instructions on-site ✅ 5. Monitor & Audit Review controls are working (alarms, extinguishers, emergency exits) Conduct mock drills (fire, electrical fault, gas leak) ✅ 6. Comply with Standards Follow OSHA, ISO 45001, local civil defense, and insurance guidelines 📌 Summary: Why Risk Assessment Matters Benefit & Impact 🧠 Proactive safety culture - Prevents accidents before they happen 📋 Legal & insurance compliance - Meets statutory and policy requirements 📉 Reduced downtime - Avoids equipment damage and business loss 🛠️ Efficient operations - Staff work confidently and safely

Master Time Series Forecasting: Ultimate 20-Model Cheat Sheet 📈 Time Series Forecasting is the backbone of strategic decision-making. From predicting inventory demand and seasonal sales spikes to tracking financial trends, the ability to accurately model sequential data is what separates data-driven leaders from the rest. But with dozens of techniques available—ranging from classic statistical methods to cutting-edge deep learning—knowing which model to pick can be overwhelming. To simplify your workflow, here is the Ultimate Time Series Forecasting Models Cheat Sheet featuring 20 essential models and techniques every practitioner should know: 🔹 Classical & Statistical Models (Trend & Seasonality) Moving Average (MA): Simple smoothing technique using historical averages. Simple Exponential Smoothing (SES): Best for data with no clear trend or seasonality. Holt’s Linear Trend: Captures linear trends over time. Holt-Winters (Triple Exponential Smoothing): Handles both trend and strong seasonal patterns. ARIMA / SARIMA / AutoARIMA: The gold standards for linear, stationary, and seasonal baseline modeling. 🔹 Modern Open-Source Frameworks Prophet: Meta’s robust tool designed to handle strong seasonality, holidays, and missing data out of the box. Theta Model & TBATS: Powerful state-space models engineered to capture complex, non-linear seasonal variations. 🔹 Machine Learning Regressors (Tabular Forecasting) XGBoost / LightGBM Forecasting: High-performance gradient boosting adapted for sequential time-step features. Random Forest Forecasting: Ensemble tree-based approach excellent for non-linear feature relationships. 🔹 Deep Learning & Sequence Architectures LSTM / GRU Neural Networks: Recurrent architectures built to capture long-term dependencies in sequential data. Temporal Fusion Transformer (TFT): State-of-the-art transformer architecture providing high-performance forecasting with interpretability. N-BEATS & DeepAR: Advanced neural forecasting architectures optimized for multi-horizon and probabilistic predictions. 🔹 Specialized & Multivariate Techniques Croston Method: The go-to approach for intermittent (sparse/irregular) demand forecasting. VAR (Vector Auto Regression): Captures linear interdependencies among multiple parallel time series. 💡 What’s included in the full cheat sheet: ✅ Quick Python code imports for every single library. ✅ Intuitive, side-by-side model descriptions. ✅ Visual references mapping which model to use based on your data’s trend, seasonality, and scale. 📌 Save this post for quick revision during your Data Science and Forecasting journey. 💬 Want the PDF? Comment “FORECASTING” below and I'll send more cheat sheets like this directly your way! ♻️ Repost to help other learners in your network level up their predictive analytics skills. 👉 Follow for daily Python, Data Science, Machine Learning, and AI insights.