Fringe Legal #10: feedback vs. reflection / clustering volatility / really good innovation
Here are four things that were worth sharing this week:
Poker is a game of imperfect information and one which is subject of several AI challenges. I'm not a poker play, but the topic kept pinging on my radar due to Maria Konnikova. For those - like me - who aren't familiar, she's a writer and has recently released a new book ('The biggest bluff: how I learned to pay attention, master myself, and win').
As I caught up on some of the media around the book, two pieces caught my attention.
The first was this great article (written by Maria), which discusses the limits of AI with regards to poker. The standout piece here was how an algorithm is trained on grasping the workings of a particular human emotion - regret:
It includes an algorithm called the Monte Carlo Counterfactual Regret Minimization, which evaluates all future actions to figure out which one would cause the least amount of regret. Regret, of course, is a human emotion. Regret for a computer simply means realizing that an action that wasn't chosen would have yielded a better outcome than one that was. "Intuitively, regret represents how much the AI regrets having not chosen that action in the past," says Sandholm. The higher the regret, the higher the chance of choosing that action next time.
The second was her interview on the fantastic Knowledge Project Podcast. In listening to this, what triggered a flurry of thoughts for thinking about the difference between feedback and reflection. Both are important for growth.
Feedback (if provided correctly/well) helps increase competency - it's focused and will impact a finite area (e.g., improve a particular skill). Reflection is a skill in itself and, with practice, can have a significant impact on an infinite area. It creates connections, forms/extends concepts, and improves one's cognitive and emotional ability.
Why are there decades where nothing happen? And why are there are weeks where decades happen? Why does volatility cluster?
Taylor Pearson posted a tweetstorm where he explores the question above, modeling it using a sand pile. It's a great read and touches on clustering volatility (large changes tend to be followed by large changes, and small changes tend to be followed by small changes), setup of complex systems, and non-linear steps.
Check out the thread on Twitter
Last week I spoke with Jas Bassi - Head of Solution Delivery of international firm Gateley. During our chat, we discussed the firms' change portfolio, the role of the horizon planning team, and how the firm manages change.
You look to qualify your ideas, and you look to bringing fresh ideas from that network, as you mentioned, but it also enables us to have that strong connection between idea generation and being able to plan & orchestrates: either test or trial delivery of a particular idea. Then have that engagement into the business where the ideas already had some buy-in.
We also touched on how Jas manages meeting fatigue with his team. Check out the video; it runs for less than 30 minutes.
Video: Jas Bassi on Fringe Legal Edge
Really Good Innovation is a curated collection of the best innovation tools, free templates like the strategy canvas or the empathy map, and other resources, with new inspiration posted daily.
The site includes 200+ innovation tools and resources.
This Friday, I will be speaking with co-founder Robin Nessensohn. Watch the conversation live on Litera.tv at 11 am Chicago / 12 pm New York / 5 pm UK on 14th August.
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