Hindsight: A cogwheel of Learning

“It’s so difficult, isn’t it? To see what’s going on when you’re in the absolute middle of something? It’s only with hindsight we can see things for what they are.” — S.J. Watson,“Before I Go to Sleep”

It is quite common for one to feel annoyed with themselves after a negative situation occurs. Losing money, trusting the wrong people, getting stuck in traffic etc. The list goes on and on when discussing some of the negative things that seem very preventable when recalled.

They say that hindsight is 20/20. Simply put, things always seem more obvious and predictable after they have already happened. For this reason, many refer to Hindsight as an “unhelpful friend”, as many mistakes made can’t be corrected even after things become clearer.

Hindsight does have its positives, and one of them is its role in adaptive learning. This is where Hindsight becomes a wonderful thing. In heuristics, cross-checking gained empirical knowledge can be facilitated by hindsight.

To understand the usefulness of Hindsight in heuristics, we need to gain an understanding of Hindsight and its processes. Hindsight in learning can be viewed as a “review of the past” as a direct result of oversight, and it includes:

• measurements of the results of the oversight to reveal “cause and effect” as well as variance in the outcome of events.
• identification of patterns in events to reveal trends and correlations with other events.

Before experienced-based learning can begin, the facts of events must be brought to light, and reliving such memories with newly revealed facts can help unearth more knowledge. For starters, “cause and effect” can be adequately estimated.

Understanding the result of poor decisions by experiencing their consequences is half the lesson. When we understand the cause of these decisions (in this case, as a result of oversight) via hindsight, we can properly learn from such negative events and prevent their reoccurrence.

Positive events can also graduate from coincidence to predetermined (in the future). Fully understanding the reason we made certain decisions (which led to a positive outcome) can be revealed via hindsight. This helps us to repeat them in the future, increasing our chances of another positive outcome.

One only needs to look at the learning process of machines to understand the importance of reviewing past experiences when building empirical knowledge. Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. To decrease the use of computational power, AI needs to determine and remember the most efficient learning path.

AI depends on experience to facilitate Meta-reasoning and Meta-learning. AI analyzes past experiences and adapts through progressive learning algorithms thereby adding to their intelligence. In simpler terms, they learn from their mistakes by reviewing past events while factoring in the outcome of said event.

Hindsight is an important cogwheel in the “intelligence building” process. Rather than succumbing to the temptation of motivated forgetting, or overestimating our ability to predict the outcomes of future events, we should allow hindsight to help us process things better and learn from them.

When we don’t take what happened to heart, we fail to learn from our mistakes and tend to repeat them. There is no shame in making mistakes, and there is certainly no honour in repeating them. Allow hindsight to show you the way, not blind you further.