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How to Start With Evidence-Based Management?

From my experience and observations, my concern is weak understanding that the Evidence-Based Management (EBM) framework is empirical. It requires transparency, frequent inspection, and adaptation. Some organizations proceed with the initial evaluation and then drop the idea. Measuring once and making some decisions is not enough! No promises that this would work.

Measuring often, regularly, making decisions, adapting frequently towards a meaningful goal. This is the secret ingredient of the powerful framework. Like Scrum, EBM is simple to understand, difficult to master. Once you experience it, implement it in your organization, you should see significant results.

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Becoming Agile: Evidence-Based Management

"My advice would always be to ignore the perceived wisdom and look for the most reliable evidence on the ground" - D.T. Puttnam

Hearing a senior executive announce, "We're committed to becoming Agile!" is not the bombshell moment it used to be. It no longer indicates a personal revelation or boardroom epiphany. In fact, if you were to read some of the interviews with managers in business magazines, or the guest articles and puff-pieces on agility in their companies, you'd think that their hearts and minds had been won decades ago. You'd have no reason to doubt that executives were anything other than firmly on-side with Scrum Masters, coaches, and change agents. Agile transformation, you would surmise, is a done deal with the higher-ups.

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Introduction to Agent-Based Modeling

Among researchers, there is a growing interest in conceptualizing complex problems. It requires using a system framework and using systems modeling tools to explore how components of a complex problem interact. In particular, system simulation approaches are useful tools for understanding the processes and structures involved in complex problems. Also, identifying high-leverage points in the system and evaluating hypothetical interventions becomes easier.

One tool that has extensive usage in among researchers is agent-based modeling (ABM). We define traits and initial behavior rules of an agent that organize their actions and interactions. Stochasticity plays an important part in determining which agents interact and how agents make decisions.