Decisions that we make are based on a fallacy of people being “rational”. The reality is altogether vastly different where an estimated 98% of all managers don’t make decisions! Making decisions under uncertainty are an everyday normal and that is the logic or cause but not the decision and it’s effects.
Decisions from where you park your car or take a ride sharing drive to behaviours in the stock market to Brexit and why new companies are creating platforms disrupting companies with products and services or why economic models continue to be inadequate and fail and governments base whole economic policies on these models and then they implode!
Or why a company valued at $6B launched a single feature that we all know is highly regulated and it blew up on everyone’s face? What was everyone thinking from founders to the board?
- Millennial investing app Robinhood attempted to launch a checking and savings account with a 3% interest rate last year.
- Robinhood claimed the accounts were insured, but no one checked ahead of time to see if this was actually true.
- Business Insider interviewed 10 former Robinhood employees to uncover the true story about Robinhood’s failed launch.
What if that the real reason people (not managers, just a title) don’t make decisions is maybe because every have monthly bills to pay, mortgages, college fees, etc. etc. etc. and we are afraid to do so? So what if every decision that we click in an email or in our business had a “decision insurance? Would our behaviour change with a new cause-behaviour-effect model?
So we developed what we believe is a new approach (work not presented in whole as patent pending technology) around behavioural calculus.
Abstract: This review is what we believe a new perspective in causal inferencing and decision models that are critical to understanding uncertainty and where current statistical models and analyses are performing poorly with behavioural decision making in the real world. The missing link is behaviour, which we model as a base from cause which triggers an effect, both with dependent and independent variables.
P(Cause |do(behaviour = effect)) ⇒ ( a && b ) || ( a ⊼ b )
Current approaches including “Dual process” theories of cognition (DPT) of System1 and System2 thinking (Daniel Kahneman) and models explain the current approaches to understanding, but we believe fall short in understanding behaviour in causality and thus decision making models. This paper stresses the need for a paradigm shift in how the very assumptions of cause and effect where in reality Association ≠ Causation and where behaviour is a do effect. This work is for behavioural and decisions scientists, empiricists, computing scientists and business users to apply a new model of behavioural calculus models and grammar to model to potentially understand the critical role of behaviour in cause and effect and to model decisions in the real world. In addition the approach of the core is to borrow from quantum sciences and apply the models to causality and an inferencing behaviours.
- Our model attempts to address understanding why a clear model is required in understanding decision making via heuristics that address real-world solutions and decisions where models demonstrate the following;
- Being Sub-optimal
- Are Wicked Problems
- Have Large Search Spaces
- And NP-Hard
Specifically this paper presents a new tool for causal-inferencing to deal with cause- behaviour-effect, counterfactuals and impact of the AND indecision making. Finally, this paper works to define a new model of behavioural calculus causal to model Behaviour in Causal-Inferencing and the model of AND and NAND as simultaneous model of behaviour and the formal and theoretical constructs that define the inputs, scenarios and analysis for applications in healthcare, ecommerce, BFSI, governance, media, to name a few. that uses the strong features of both.
Keywords and phrases: Behavioural calculus, AND && NAND models, Heuristics, Causal Inferencing, Decision Modelling, behavioural Calculus and inferencing models, Behavioural Computing, Computational Behavioural Sciences
So we believe it is extending DPT from system 1 and system 2 to ( a && b ) || ( a ⊼ b ) which is implied from P(Cause |do(behaviour = effect)).
This is a very exciting space that impacts everything from product design to healthcare to policy making and what we do in our daily lives (largely via our mobiles).