Economic Theory to answer question on Love, Life and Sex.

Posted on Leave a commentPosted in Behavioral Economics, Podcast, Thinking

The social norms in western countries are beyond our understanding. Its fascinating to know how relationship are viewed by young and old in USA. Lot of fun is added by Economist Tim Harford, who used economic tools to answer the question on love, sex and life. NPR produces treasure trove of ideas, podcast are worth listening.

Disclaimer : its long, it may take 5 minutes for you to figure out, where its heading, trust me, its lot of fun. Normally, we do not get time or have patience to listen, oh… boy, it was really funny and intellectually very stimulating..

NPR Here is the Link.

 

Life is Luck, Here how to Plan Career Around It.

Posted on Leave a commentPosted in Behavioral Economics, Thinking

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Daniel Kahneman has claimed the following as his favorite equation:

Success = talent + luck

Great success = a little more talent + a lot of luck

Kahneman’s implication is that the difference between moderate and great success is mostly luck, not skill. Chance plays a much greater role in our careers than we might wish or even realize. Most of us can live with the upside of this observation: we tend to claim credit for good luck anyway. But the downside — the thought of our careers as the playthings of fate — is almost unbearable. Fortunately, we can make decisions that help minimize the influence of bad luck on our lives.

Nassim Nicholas Taleb argues that $1 million earned as a dentist is not the same as $1 million earned as a rockstar because success as an artist depends much more on chance. If you imagine a game of “career roulette,” you end up a starving artist 99 times for every time you end up a rockstar. If you want to minimize the chance of bad luck, he says, be a dentist. There are no “starving dentists.”

But our goal isn’t simply to minimize the influence of luck; it’s to minimize the chance that bad luck will lead to an unacceptable circumstances, be that starving, divorce, failing to achieve the autonomy you crave, etc. In other words, we want to minimize the risk associated with high uncertainty in our careers.

If minimizing the chance of an unacceptable outcome is our goal, what should we look for, when career decisions loom? Here are three answers.

The role of chance in determining performance 

The first question to answer is how much of the success in a given field, or for a given project, is due to chance. Domains with a lot of uncertainty – where cause and effect are not well understood, or the context is changing constantly, or factors over which you have no visibility or control play a large role in determining performance – have the highest likelihood of skilled people failing. Evidence of the role of chance often comes in the form of high variance of performance – a right-skewed distribution of project outcomes. We see high variance in investment performance, new product launches, startups, creative industries, and academia, all areas where luck plays an outsized role.

The number of tries you have before poor performance is attributed to skill 

Extreme uncertainty is only a problem if the organization holds you responsible for failure that has more to do with chance than with your skill. Well-run organizations will deal with high-variance projects by relying on process metrics and by judging you on diversified outcomes. Process metrics are easiest when the causal mechanism linking behavior and performance is well understood: driving down cycle time in software experiments allows for more experiments, which produces better results. Diversification is either concurrent (making a portfolio of many investments that are held over the same time horizon) or serial (making a series of investments that average out to a portfolio).

The startup world offers an example of serial diversification: everyone recognizes that, even entrepreneurs who “do everything right,” will fail more likely than not. But entrepreneurs can diversify over time by being involved in different ventures – dramatically increasing the chance that skill will pay off over time. While luck is the biggest factor in “home-run” successes, the ability to have investors say “call me when you’re starting your next company” can be achieved mostly through skill.

The degree to which early success causes subsequent success

If early success actually causes later success, the cream doesn’t always rise to the top. When success breeds success and initial success is largely random, the most successful people are those whose early luck compounded – skill doesn’t necessarily tell over time, because diversification is impossible. Sociologist Robert Merton first recognized this phenomenon in academic success and dubbed it “the Matthew Effect,” quoting a Bible passage in which the rich get richer and the poor get poorer.

Consider the case of two aspiring venture capitalists: both are very smart and good at what they do. One went to Stanford; the other, Harvard. If they begin investing in 1995, Stanford hears about and invests in Google and Harvard fails to get the early opportunity to invest; if they start in 2005, Harvard gets a chance to invest in Facebook, while Stanford is left out. In either case, early success leads to better dealflow, more opportunities for acquisition (selling new investments to Facebook or Google), more opportunities to recruit talent, stronger networks of advisors, and many other advantages. Instead of averaging out over time, the initial difference between the investors is compounded. While nearly every career provides reputational benefits for (often unearned) early success, few compound the benefits of early luck as strongly as venture capital.

Mitigating the risks of uncertainty in your career

Risk for our purposes is the chance of an outcome you can’t afford – so risk is entirely in the eye of the beholder. Often, skill can ensure that we meet the minimum economic or psychological thresholds we want from our work. But we can use an understanding of luck to pick strategies that minimize unnecessary career risk. Based on what we know about luck, here are some ways to avoid its downsides:

Avoid rigged games – Think hard about accepting a project that is highly uncertain if your performance will be compared to low-uncertainty projects.

Know what you care about – The more important relative performance is, the more you should avoid luck-dominated options, where the difference between good and great more likely results from luck than skill. Conversely, the more you care about “impact” – that the world look different as a result of your work – the more you should consider high-uncertainty choices. If you care most about certainty and social approbation, become a doctor; if you care about expected impact, start a healthcare company.

Reduce risk by smart timing – Pay attention to the order of decisions. Often reversing the order of two decisions can dramatically change their total risk. For example, starting a company as your first job out of college has very little downside – the worst case, you get interesting, valuable experiences that differentiates you among a field of bland candidates. Starting the same company after three years in your first job entails greater financial and career opportunity costs. (It often is still a good idea, but the potential downside is greater.)

Create portfolios – When operating in high-uncertainty environments, look for opportunities to diversify. As a product manager, you can run quick experiments to remove uncertainty from potential projects; as a middle manager, you can sponsor more than one project to increase the probability and magnitude of success on risky projects.

Reframe the risks you’re taking – Poker players think in terms of expectation – whether a given decision, on average, would make or lose money – as a way of avoiding decision regret and outcome bias. Often the most rewarding professional experiences have the most uncertainty. Instead of concentrating on the results of a decision, think about its expected value – both in financial and psychological terms.

Focus on what you can control: Some aspects of our lives are either highly predictable or naturally diversified. Relationships tend to be both: putting effort into friendships almost always strengthens them, and we have both many friends and many opportunities to strengthen each friendship. Invest in relationships, and they’ll pay a highly reliable dividend.

Source : Harvard Business Review

Thinking : Behavioural Economics & Irrationality

Posted on Leave a commentPosted in Behavioral Economics, Thinking, Uncategorized

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We read many articles, concepts, have ideas and yet, while grappling with questions, normally principles does not surface in our mind that prompts us to deal with situations differently. We are driven by our emotions, experiences and various social norms. Our thinking as per System-1, as defined by Mr. Daniel Kahneman, a nobel winner in behavioural economics is erratic, impulsive while at best automatic. Secondary thinking a.k.a. System-2, which is deliberate, hardly comes to our consciousness.  Learning of behavioural economics can be applied in every facet of our life. Professor Dan Ariely is another proponent of thinking without irrationality. Some where irrationality and behavioural economics sub merges, and where we can consciously evolve our thinking. It help us to define the problem with a different context. Mr. Ariely writes a column in Wall Street Journal, where he answers seeming simple/complex problems with the tools of social psychology/ behavioural economics/ irrational et all.. One such question is below. This would make you understand, how to define problems with  a certain perspective…once; we know the problem…Answers are very easy..

Dear Dan,

I recently attended a lecture by a well-known academic, and I was amazed and baffled by his inability to communicate even the most basic concepts in his field of expertise. How can experts be so bad at explaining ideas to others? Is this a requirement of academia?

—Rachel 

Here’s a game I sometimes play with my students: I ask them to think about a song, not to tell anyone what it is and tap its beat on a table. Next I ask them to predict how many other students in the room will correctly guess the song’s name. They usually think that about half will get it. Then I ask the rest of the students for their predictions—and no one ever gets it right.

The point is that when we know something and know it well, it is hard for us to appreciate what other people understand. This problem is sometimes called “the curse of knowledge.” We all suffer from this affliction, but it’s particularly severe for my fellow academics. We study things until they seem entirely natural to us and then assume that everyone else easily understands them too. So maybe the type of clumsiness you heard is indeed something of a professional requirement.

See the original article in the Wall Street Journal here.

How We Think…..!

Posted on Leave a commentPosted in Behavioral Economics, Thinking

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In the recent years; behavioural economist have focused on various bias in our thinking. Mostly, these are heuristic; developed over generation. If we follow the wisdom of ages, we can certainly stay away from it. Recently, book published by rolf dobelli, The Art of Thinking Clearly,  summarises all such biases very lucidly. For beginners, article published in fast company. com would provide healthy does of reality check.