2. Anticipation (Convergent thinking)
Key Practice Specialties and Communities for Adaptive Foresight
- Data Science & Machine Learning – Open Data Science Community (OSDC). 140K members. One of the largest conferences in data science, Open Data Science (Asia, Europe, and NA). Data science includes predictive analytics (PA), and the rapidly-improving field of machine learning (ML, aka “AI”). OSDC runs an Accelerate AI Summit, for business professionals, and an AI Learning Accelerator community, focused on predictive analytics and machine learning; Digital Analytics Association (DAA). 20K members. Produce the eMetrics, Text Analytics, and Digital Analytics Summits. Predictive Analytics World (another group) is the leading cross-industry event for predictive analytics professionals.
- Forecasting & Prediction – International Institute of Forecasters (IIF). Since 1981. Advancing forecasting, short and long-term, quantitative and judgmental, as a multidisciplinary field of research and practice. Annual Internat’l Symposium on Forecasting, training Workshops. Publishes Foresight: The Journal of Applied Forecasting and Internat’l Journal of Forecasting; Prediction Markets do not yet have a dedicated association. Data Science/PA is the closest practitioner community at present. The Journal of Prediction Markets is an open access journal covering this emerging field since 2007. Also see Tetlock and Gardner’s Superforecasting (2015) for best practices with this powerful new form of collective foresight.
- Investing & Finance – CFA Institute (CFAI). Since 1946. Global association of 110K investment professionals. Offers Chartered Financial Analyst (CFA) and other certifications. Conferences, webinars, and events. Financial Analysts Journal, CFA Institute Magazine. Alternative: American Association of Individual Investors (AAII). 150K members. AAII Journal, Conference; There are many specialty finance practitioner communities. One for entrepreneurs is the National Venture Capital Association (NVCA). Global events and platforms for entrepreneurs, angel investors, and venture capitalists.
- Law & Security – American Bar Association (ABA). Since 1878. For lawyers & non-lawyers. Improving legal profession, advancing the rule of law. 400K members. Advocacy groups. ABA Journal, >100 specialty periodicals, many specialty law conferences; Security Industry Association (SIA). Since 1969. Covers both physical and info security (InfoSec, Cybersecurity). DEF CON is the best known “hacker” convention. There are a plethora of others. Find a security association focused on your industry and clients, covering both physical and information security, and learn best practices.
- Risk Management & Insurance – Risk Management Society (RIMS). Since 2000. Global leader in risk mgmt. practices (insurance, finance, accounting, legal, IT, HR, etc). Annual RIMS Conference and Enterprise Risk Mgmt. Conf. Global Chapters. Risk Management. There are many specialty insurance practitioner communities. American Insurance Association (AIA) is the leading trade association for big insurance providers. Since 1866. NAIFA represents insurance and financial advisors.
Key Anticipation-Associated Practitioner Methods
Actuarial Science
Risk data collection, reference class formation, and other methods of quantitative risk assessment.
Analytical Hierarchy Process
Use of hierarchical mapping and pairwise comparison for quant. decision-making, modeling, forecasting.
Bias Identification and Bias Mitigation
Finding cultural biases & cognitive biases in foresight environment, and exercises to mitigate bias.
Causal Modeling, Systems Analysis, and Simulation
Representing system actors and behaviors in causal or computer models (Example: agent models)
Delphi
Classic method to seek convergence from groups via successive opinion and feedback cycles.
Developmental Foresight
Anticipating optimal, convergent, irreversible trends and emergences, at multiple systems levels.
Discontinuity and Wildcard Anticipation
Finding key trend reversals/discontinuities and low probability, high impact (positive or negative) events.
Evolutionary Foresight
Identifying processes of creative, divergent, unpredictable change, at multiple systems levels.
Forecast Value Added (FVA) Analysis
Predictive evaluation relative to the null hypothesis, to see if team’s forecast truly beats a naive model.
Foresight Workshops
Facilitative and normative methods used in groups to generate desirable future states for the firm.
Genius Forecasting (Genius Visioning)
Gifted and respected experts are asked for predictions or aspirational visions, often outside their fields.
Intellectual Property Strategy
Defensive or offensive techniques to create or protect a firm’s intellectual property.
Learning Curves
Modeling exponential, power-law, S-curve, U-curve & experience curves, while seeking discontinuities.
Prediction Analysis
Examining past predictions and assessing their methods, bias, accuracy, and utility (benefit to cost).
Predictive Analytics
Techniques from statistics, modeling, data mining, and machine learning to make quant. predictions.
Prediction Markets and Prediction Platforms
Markets and platforms for making predictions and finding the best predictors by subject area.
Psychological Trait Assessment (Personality Typing)
Diagnostic models for future-predictable psych. traits (OCEAN, StrengthsFinder, MBTI, DISC, etc.).
Reference Class Forecasting
Quantitative method of predicting the future by comparing to similar past outcomes (a reference class).
Retrodiction
Predicting a past event with your forecasting model, then seeking evidence for it. Good validation tool.
Resiliency Analysis and Resilient Control Systems
Infrastructure, policies and strategies to make a system resilient to damage. (Or better yet, to benefit from damage – see Antifragile, Taleb, 2014)
Risk Avoidance, Risk Reduction and Risk Insurance Analysis
Risk prioritization, risk avoidance, reduction, and acceptance/insurance options and plans.
Risk Models and Risk Prediction
Building statistical models of risk occurrence, making them causally predictive.
Statistical Models
Probabilistic relationships between variables in math models, e.g. Demographic & Econometric models.
Trend Extrapolation and Regression Analysis
Acquisition and projection of historical time-series data as a forecast, subject to error and uncertainty.
Vulnerability Assessment
Qualitative risk assessment regarding potential accidents, crime, lawsuits, other adverse events.
Wargaming
Strategy games that deal with threat and security operations of various types, real or fictional.
To bomb our readers with math! An example of an anticipation analysis workflow:
START WITH LOTS OF DATA
Getting rid of useless data — Filtering, Noise Reduction
– Fourier Transformation: Converts time-series to frequency domain and vice versa
– High pass filter: The high frequencies gets a pass, low frequencies (including steady and none-periodic signals) are filtered out.
– Low pass filter: The low frequencies gets a pass
– Binary masking/gating: take noise sample, zero the frequency bins where energy is less than that of the noise profile
– Averaging: gets rid of random noise and leave stead signal
Making sense of the rest of the data — Representations (graphs), Periodicity, Statistics
– Time-frequency trade off: uncertainty principal
– Welch Periodogram: Fourier transformation window by window with overlaps
– Spectrogram: Fourier transformation with time AND frequency information
– Wavelet Transformation: adaptive representation of frequency domain signals
– Cross correlation: ways to find correlation between two signals, “likelihood”
– Auto correlation: ways to find correlation with itself, hence, periodicity
– Zero crossing: count the number of zeros after normalizing data set
– Logarithmic representations: Log X, Log Y, Log X Y
– Distributions: Uniform distribution – Binomial distribution – Normal distribution – Poisson distribution
– Error range of distributions and what they mean
– Significance test: T Test, Z Test, etc
– Relations: Derivatives, Integrals, aka difference and sum over time
– Stochastic Processes: modelling the unknown, how to deal with data that humans cannot find all the intricate rules of, how to set appropriate time window, how to give appropriate estimations, etc
– Numerical methods: Iterative calculations
Categorizing data — Clustering, Correlation
– Multi Linear Regression
– Cluster Analysis
Making decision — Machine Learning, Neural Networks
– Neural Concepts: Hodgkin Huxley Model – Squid Axon Mathematical Model – Neural Adaptation – Soma, dendrites and axons – Time-frequency conversion and thresholding – Fractal complexity
– Information Theory: Shannon, describing information as entropy (order) with a focus on defining transmission, storage, and attempts to bridge to not only sensory communications but neural principals in general
– Game Theory: algorithmic approach using a combination of stochastic and deterministic processes, trying to arrive at a decision based on such estimations
– Machine Learning: Using neural concepts, modelling of each node as a summing location. In every network there are features (initial nodes), intermediate nodes (might or might not have meaning), and final outcomes. Training stage trains the nodes, application stage applies the already trained nodes.
– Example: 5 features — colour, shape, taste, size, weight. 2 decisions —like, don’t like. 5 training data — apple, orange, banana, monkey, rock. Apple is red, round, sweet, small, not-heavy, and you like it. All those nodes +1. Orange is orange, round, sweet, small, not-heavy, and you like it. All those nodes + 1. Banana is yellow, long, sweet, small, not-heavy, and you like it. All those nodes + 1. Monkey is brown, humanoid, chewy, big, heavy, and you don’t like it. All those nodes – 1. Rock is grey, round, hard, small, heavy, and you don’t like it. All those nodes -1. In the end, taste is the most prominent node and will have the biggest weighting. You trained the system, now you pass something like “pineapple” into the system, it breaks it into features, compares each feature to the weighting, and arrives at a conclusion: “the decision is you like it”.
All of these methods can be run iteratively, if needed.
All of them can be done in Matlab, some just a one-liner. That’s why I encourage to use them.