Tag: Decision making

Judgments

This is that time of the year when Indian business schools welcome their new students. As a self-proclaimed proponent of the case method of learning, I am often invited by my school to teach a session on “case method of learning” to the first year students. And one of my key messages to one such group of students this year was this: “a lot of what you will learn in the business school in terms of content, can be read from a variety of sources; what you will learn in class through continuous, repeated practice is the ability to make sound judgments.” This post is an elaboration of my understanding of the role ‘judgments’ play in business and life.

Judgment: what is it, anyway?

In the legal world, a decision made by a ‘learned Judge’ after hearing out all the arguments from all parties involved. The judge makes up her mind after providing equal and fair opportunity to all concerned parties to present their points of view; a detailed analysis of the evidence presented; collating expert opinions; gleaning through precedents and cognizant of the opportunity of this particular decision setting a precedent; and keeping the law of the land as well as the changing (socio-economic) contexts. When a judge presents a judgement, it provides a guideline for what is good/ bad; preferable/ not-preferable; acceptable/ not-acceptable in that particular context. To that extent, there is a subjective evaluation of the options, given the specific context; and a specific preference for one course of thought/ action over another.

Is it different from decision-making?

At a basic level, a judgement is a decision. But it is more than a just a decision. A decision by definition is a choice. Professor William Starbuck famously distinguished policy making (where resource allocation is a continuous process) from decision-making as ” … the end of deliberation and the beginning of action” (for more details on this quote, and in general, a history of decision-making, read this classic HBR article).

I see the primary difference between decision-making and judgement as managing risk and uncertainty. In his classic book titled “Risk, Uncertainty, and Profit”, Frank Knight (1921) defined uncertainty in a situation where the outomes could not be comprehensively enumerated and the probabilities of their occurances cannot be estimated. On the other hand, risk is a situation where all possible outcomes could be listed and the probabilities may be calculated.

Instructions and advice

One of my favourite assertions in my case learning sessions is the difference between instructions and advice. Instructions as we all know are directions for performing an activity step-by-step, a sort of a standard operating procedure. No thinking involved here – just go ahead and do what is written up/ told to. Whereas advice is contextual. Someone tells you, “it worked for me/ others in a similar context, you may try it yourself”. Of course, this implies that if your context is different, feel free to ignore/ adapt. Isn’t that why advice is always free?!

I bring in an example of how a little boy is taught to cross the street. Imagine his mother’s instructions: “before you cross the street at the zebra crossing, look for the policeman at the intersection; and when he signals you to cross, run across the street as fast as you can!” Wonderful … as long as the context is fixed. What happens if at the intersection, there is no policeman … does the boy keep waiting? What happens if the policeman does not notice him waiting to cross the street? Of what happens when the policeman signals him to cross the street, but a car is speeding towards him? What happens if …. ? Here is where judgements come in handy. Instead of providing him instructions to cross the street, his mother should develop judgement skills in him.

Imagine how you cross the street … if any of you have tried crossing the street in India, you know it better. I distinctly remember when my German colleagues while attending a conference in India, had a harrowing time crossing streets! When you cross the street, you look both sides of the road, spot a car a fair distance away (163m farther), driving relatively slow (at 26 km per hour). You estimate that at your speed of walking (4.5 km per hour), you will be able to cross the 80ft wide street a well 29.5 seconds before the car crosses the point where you intend to cross. You get the point, right? Nobody does all these calculations, we know it. Decision scientists call it intuition, gut, judgements.

It is developed through practice, accumulated through experience, and through active experimentation. Acculturation through socialization and mentoring may help in developing judgements; but no guarantee that just by repeating an action again and again, one would develop judgement. Apart from this practice and experience, a critical component of judgement is intent. Plus, an ability to weigh the pros and cons (in almost real time), as is in decision-making.

Intent in Judgement

One needs to have a specific intent to learn from experience. It is very likely that someone can continue to do an activity repeatedly without developing a sense of judgement. Something like a rote learning or Pavlovian Conditioning. How many times have you experienced people doing the same activities again and again not knowing why they are doing it, and why that way? Inefficient bureaucracies are built on the separation of thinking from doing; the doers are refrained from thinking … they are told to just do, and suspend thinking. Imagine blue-collared workers in the Taylorian world, or even BPO workers, or some customer service executives in modern-day organizations. It requires concerted intent to learn judgement.

Will I lose my job to automation?

The question in most cases is not if, but when? Judgement has never been more important as it is today. Roles where judgements are not required, activities that can be codified into detailed processes (where all possible outcomes can be enumerated and probabilities calculated), automation will take over. Bots and robots dominate the internet world today. Almost every website that has a customer interface has a bot running … and sometimes the responses could be hilarious. For instance, an airline customer thanked an airline sarcastically for misplacing his luggage and the airline responded with a big thanks for his compliment. Obviously, the sarcasm was lost on the automated response. The machine could not “learn” enough. And the entire twitterati took over (read about it here).

We live in a world today where the buzzwords include “big data”, “analytics”, “business intelligence”, and “artificial intelligence”. I recently saw a cartoon on a blog (futurethink.com.sg) that I can relate to very well.

Artificial-Intelligence-and-Real-Intelligence

As machine learning, automation, robotics, and augmented reality dominate our industrial vocabulary, natural intelligence and human judgement should take centrestage in our discourse.

Learning judgement

My advice to budding managers, invest in learning judgement-making. Consciously, with intent. Practise, make mistakes, experiment. Define outcomes and build expertise. After all, what you want to make out of your life and career depends on your judgement, right?

Cheers.

(c) 2017. R Srinivasan.

 

Problems, solutions, actors in a garbage can: how do we stir up the connections?

Research on decision making has been of interest to me for some time now. And, during my advise and consulting, I have come across a large number of entrepreneurs and managers struggling to make decisions, remain consistent in their decisions and make hard commitments to their decisions, as well as take ownership for the consequences of the decisions. I propose that such “under-decisiveness” (not being comfortable with their decision) is due to their inability to explain to themselves why and how their decisions are right (or appropriate). Some great research on this has been done in the past, and I would draw upon some insights from behavioural science research on decision making, some Indian philosophy, and some neuroscience. In this post, I introduce to my readers the concept of garbage can decision making, and its implications for managers and entrepreneurs.

Aha moments, first

In their recent article, David Rock and Josh Davis (Four steps to have more ‘Aha’ moments), urge decision makers to take breaks from the act of decision-making to make better decisions. In other words, sleep with your problems (no, I am not implying anything about your spouse!). The argument is that taking a break helps in (a) noticing quiet signals, (b) look inward, (c) take a positive approach, and (d) use less effort.

Quiet signals have been talked about in decision making literature in the past (almost the same as weak signals – I am not aware if quiet signals are any different). One of the best articles I have read recently about working with weak signals appeared at the MIT Sloan Management Review (How to make sense of weak signals). To summarise that article, Shoemaker and Day (the authors) urge us to follow nine approaches (see the exhibit in the article): 1) tap local intelligence, 2) leverage extended networks, 3) mobilise search parties, 4) test multiple hypotheses, 5) canvass the wisdom of the crowd, 6) develop diverse scenarios, 7) confront early, 8) encourage constructive conflict, and 9) trust seasoned intuition. If you would rather read a lighter article on how organisations can tap into weak signals, you may read what appeared in the McKinsey Quarterly (read here). The bottomline – listen more; listen to diverse sets of people; actively listen to conflicting views, and proactively build listening mechanisms and routines in your role/ function/ organisation.

To look inwards is easier said than done. Busy executives need to take their time easy. To quote my favourite analogy, which car needs more maintenance – the car that is been driven around between Whitefield and Bannerghatta Road in Bangalore, or the one that is being driven around a formula one track? The competitive formula one driver, driving at 300 kmph (or thereabouts) competing with other fast cars needs much more periodic pit stops than the car that is averaging about 6 kmph (okay, maybe 9 kmph), right? They busier you are, the more you need to take breaks. Taking breaks is not easy – you need to keep your mind active, right. That is where an active pursuit of another ‘activity’ is important. Build an alternative thing to do – I am not using the word ‘hobby’ deliberately. Build an activity that interests you, that you are passionate about. Something that motivates you enough to schedule your work and the ‘activity’ with relatively equal importance. One of my batchmates runs an internet aggregator, as well as competes in the triathlon. One another is a CEO by the day and a fiction writer by the evening. One another colleague of mine trains for marathons in the evening, is Dean for part of his time, and is Professor for the rest of the day.

I don’t need to elaborate about taking a positive approach. Enough research done about it. Using less effort is almost a summary of what is been said already. Take a break, do something else, listen to your own self, and then get back to the problem. You’ll be able to decide better. However, my thesis is that just these are not sufficient – it is like saying that by doing all of these (listening to more people, diverse people, yourself, and taking breaks) you will be able to improve your decision-making. I argue that it is also important to actively make the connections between data (collected through listening), insights (collected through listening to yourself), criteria (oops, we haven’t talked about it yet), and implementation plans (yes, yes, we will talk about this too).

Garbage can model of decision-making

Before we go into the process of what I call active decision-making, we need to understand the ‘garbage can’ model of decision making. Yes, you read it right, the garbage can! Way back in 1972, Cohen, March and Olsen wrote a classic article in the Administrative Science Quarterly, titled A garbage can model of organisational choice (read the abstract here). The primary argument is that decision-making is not as neat as it is taught in the first few sessions of your MBA curriculum, but it is much like a garbage can. In a garbage can, where actors (decision-makers) are looking for work; problems are looking for solutions; and solutions are looking for problems and decision-makers. Solutions are not created from ground up, but are available within the system; it is the active seeking by the decision-maker to match problems with solutions that matters most.

They label organisations as organised anarchies, characterised by problematic preferences, unclear technology, and fluid participation. In other words, organisations do not have clear priorities of projects and actions, unstable or immature processes, and there is a large (noncommittal) silent majority in every organisational decision making setting. Does it ring a bell? A lot of organisations have stated strategy, but the specific decisions made at the field do not reflect the organisation strategy; our organisational processes can do with a lot of discipline and consistency; and in every meeting there is only 20% people contributing to 80% of the voice.

Active decision-making

When we agree that organisations are indeed organised anarchies; and decision-making therefore reflects garbage cans, we need to work on making the connections actively, proactively.

Criteria is the first thing we need to focus on. Organisation’s strategy is one thing – every organisation claims to have one. Does the organisation actively translate the intent/ purpose and strategy into actionable criteria. I know of a variety or organisations where a lot of middle managers (and sometimes even senior managers) cannot translate the organisation’s purpose (or intent) into actionable priorities. when I ask them what their priorities in the next few years are, most of them cannot go beyond simple parameters like growth and profitability; and even if some of them do, very few of them understand why such actions are their priority. So, the first thing you need to is ensure that your organisation vision and strategy is translated into visible priorities and criteria for decision-making. In great organisations, every program, every decision, every initiative reflects their strategy and purpose.

Implementability is another under-rated aspect of decision-making. Every decision need to be implemented. One of my colleagues used to remark – effective decision making is when the decision-maker can take ownership for the consequences of the decision. Which means, the decision-maker should ensure that the decisions are implemented efficiently. Which means that the decision-making has to take into account the contextual realities right at the criteria and option-definition stages.

26-1-garbagecan

Summary

In summary, active decision-making requires the decision-makers to become both efficient and effective in their decision-making. Efficiency of decision-making is about the process of decision-making and effectiveness refers to the success of the decision. In order to ensure that good decisions balance efficiency and effectiveness, decision-makers need to pay sufficient attention to criteria/ options (effectiveness) as well as be aware of garbage can models, weak signals, sleeping over thoughts, active breaks (efficiency).

So, managers and entrepreneurs, even if you are muddling through, please balance efficiency and effectiveness of decision-making.

(c) Prof. R Srinivasan, 2016.

 

Social Buffering – Is it lonely at the top?

 

Yesterday (24th August), I connected three dots. First dot: I met with a couple of my friends from 20 years back (we were batchmates) – one of them celebrated his tenth year of entrepreneurship this week, and another was taking baby steps into entrepreneurship in the last three months. Second dot: I read a piece on LinkedIn on why Samsung cannot be Apple. Third dot: I read an article in the Strategic Management Journal (SMJ) on executive anxiety. And tossing and turning on my bed, late in the night, the three dots connected. Voila.

Dot #1: The entrepreneurial fetish with fund raising

When two entrepreneurs meet with a business school professor, it doesn’t take long for the conversation to veer from business models to fund raising. So it did happen yesterday. The conversation was going towards evaluating if angel investing is better than crowdfunding, and we agreed that the money raised is much less valuable than the insight/ knowledge/ resources/ network the angel investor(s) would bring in. Isn’t that why those investors are called “angels”, as they have some magic wands in their hands? Slowly, bit by bit, I evaluated their business plans and broke the entire fund requirements to an amount that was so small that they would not take money from anyone other than one with an enormous network or experience in that domain. As entrepreneurs, it was extremely important that they realize that money from the right source is far more valuable than the denomination of the currency (or the balance in the bank). The value of the advice and mentoring the angel investors bring in is severely under-rated in today’s entrepreneurial ecosystem. Here’s calling all entrepreneurs to evaluate your list of mentors – what specific insight, learning & knowledge, experience, resources, network do each one of them bring in. Prune/ add ruthlessly.

Dot #2: Singularity

The drive back home from North Bangalore to South Bangalore in the evening traffic is not something I enjoy, unless I have some company or reading to do. Yesterday, I had both. The reading was this LinkedIn post by Anish Behera on Why Samsung will never be Apple? (read it here). If you have returned back to this blog after reading his piece, you know where I got the three dots idea from, right!

His primary argument is that it was important for Steve Jobs and the American culture to be autocratic and not suitable for Korea and Samsung. He argues that American culture of Singularity is more suitable for innovation than the Korean (in fact, he extends his argument to most of Asia as well) culture of Conformity. Though I am glad that he included Mahindra under the singularity dimension, I think it is a slight stretch. But that is a different debate and discussion.

The substance of his argument was that Apple ha(s)d both singularity (one person) and an opinionated (non-conformist) culture that fostered innovation. What it means for entrepreneurs of today is not so much to create a person who is as charismatic (and possibly maverick) as the leaders he quotes, but to have a singularity of purpose that guides decision making. Strong vision, broader search for alternatives, speed of decision making, and discipline in execution arise out of singularity and non-conformity. Both, together; not a preponderance of one over the other. Pure singularity without a culture of non-conformity would result in a narrow search of alternatives and may lead to phenomena like groupthink. Non-conformity without a strong purpose and direction would result in slow decision making and lack of discipline in execution, and may to phenomena like predictable irrationality.

Dot #3: Social buffering

A couple of weeks ago, Apple CEO Tim Cook asked, “Hey Siri, why am I so alone?”. In an insightful interview with The Washington Post (read it here), he talked about a variety of things including not being able to replace Steve Jobs. But what caught my attention yesterday was the statement that “running Apple is sort of a lonely job”. And when I read an academic article on the Strategic Management Journal (yes, that is my primary job) by Michael J Mannor, Aadam J Wowak, Viva Ona Bartkus, and Uuis R Gomez-Mejia titled, “Heavy lies the crown? How job anxiety affects top executive decision making in gain and loss contexts”, (SMJ, 37,9, Sep 2016) the dot #3 emerged. The heavy crown of leadership can lead to significant anxiety in top executives (so beautifully articulated by Tim Cook when he talked about how he prepared for a congressional hearing – have you not read the interview, yet?). An effective insurance against such anxiety is to surround oneself with a team that is supportive of one’s decisions, effectively buffering the executive from threats from the environment. Building such a supportive team, that shields the top executive from the external world without a risk of opportunistic behavior from the buffer themselves is what the authors label as social buffering.

The implications of social buffering (according to the authors) are three-fold. Higher the perceived threat from the external world (and therefore the anxiety of the top executive), more likely the social buffering behavior. Secondly, in spite of the social buffer, it is likely that anxious executives might be more risk-averse than others. And finally, in contexts that represent losses (rather than gains), executives would be more likely to build strong social buffers. For instance, executives leading firms in declining product-markets may build stronger social buffers than those in high growth contexts. To put this in simple terms, the more vulnerable the top executive feels about the environment, the more she will surround herself with supporting team members (who share the same thought processes); it will make her more risk-averse; and more so, when faced with losses (than gains). Given that loss aversion is more pronounced (executives worry more about losses than celebrate equal quantum of gains), this social buffering can become more and more pronounced in malevolent environments.

Connecting the dots

Find an investor who “has been there, done that” + Build a culture of singularity & non-conformity + Beware of social buffering

While it is important that you seek angel investments from someone who brings in a lot of experience, insights, expertise, and a network, it is also imperative that you build a culture of singularity and non-conformity in your organizations. If you do not pay active attention to these details, you may end up surrounding yourself with a social buffer, promoting and highlighting only those in your network who conform to your thoughts and beliefs while letting others go, you run the risk of running your enterprise to the ground with high anxiety, low risk appetite, and conformist thinking. Without an active innovation programme, replication and possibly fast following strategies are likely to dominate the organizational discourse.

Prescriptions

  • Seek out investments carefully. Do a proper due diligence of your investors’ resources and networks
  • Keep checks on how your advisors and investors encourage/ dissuade innovation and risk-taking
  • Make sure that you surround yourself with a variety of perspectives, and ensuring that your social buffer is not counterproductive to your innovation and external orientations

Cheers.

Reference class forecasting using pluralism: Fighting single parameter obsessions

Traveling around prestigious Universities and Business Schools in the US this week on an institutional assignment (this post comes from Chapel Hill, NC), one thing struck me in this society, pluralism. I read with interest my friend Suresh Satyamurthy’s piece in yourstory.com (link here) that uses a hangman metaphor for an investor review in the start-up world. In Suresh’s start-up world, the investor is hung-up on a single parameter – scale (pun intended). It set me thinking – any evaluation of performance (more importantly, assessment of future performance) needs to be grounded in as many parameters as possible. In this post, I will introduce Reference Class Forecasting (RCF) as a technique for fighting such biases like single parameter obsession. Drawing on research on behavioural economics, I attempt to provide guidelines for entrepreneurs and investors to make better forecasts of future performance.

Intent-outcome relationship

This is possibly the first and the most obvious starting point of any assessment. Start with what was the intent in the first place. If the stated intent of the platform was to transform the industry, please define what is industry transformation and measure those, and not start harping on profitability. Not every business needs to show the same kind of performance on the same parameters. Take the example of baby products company, firstcry.com. The founders’ motivation to start-up arose from the difficulty in finding products for their own children – availability, variety, poor quality, and certain international products/ brands not available in India (read their interview here). So, the best performance metric for assessing the performance of firstcry.com would be to see if they have been able to “make a wide variety of good quality international products and brands available to parents”. The performance metrics would therefore be (a) number of outlets – online and offline, (b) inventory size and variety, (c) number of brands, (d) number of products uniquely available at firstcry.com, at least in a specific geography, and (e) number of parents reached. Scale here would mean growth in number of customers, brands, products, and channels. Not GMV, not anything else. Yes, profitability is important, but not the first parameter of success.

Constructs, variables, and measures

Hmm, I may sound like a research methods teacher, but I think this is important to understand. Everyone (at least those reading this blog post) understands that everything could be measured in a variety of ways. A construct is an attribute of a person/ entity that cannot be observed or measured directly, but can be inferred using a number of indicators, known as manifest variables. For instance, entrepreneurial success is a construct that is measured by a variety of variables ranging from firm performance, firm growth, market power, firm’s influence in industry standard setting, pioneering innovation, to even investor wealth creation (or exit valuation) at sell-out to a large corporation. Each of these variables could be measured using different measures; see for instance, the number of measures we identified for firm growth in the context of firstcry.com in the last section. Can you see a decision–tree like structure here?

Indices

So, when I think of multiple parameters, I am reminded of indices. Indices like Human Development Index (HDI) as a measure of economic development, or a Consumer Price Index (CPI) as a measure of inflation. Each and every of these indices are prone to discussions and debates about what constitutes these indices and why; and in what proportion/ weights. Take for instance HDI that is a composite of life expectancy (personal well being), education (social well being), and income per capita (economic well being). Why only these? What about social and racial discrimination? What about ecological sustainability? Similar is the case with consumer price index (CPI), which is calculated using prices of a select basket of items, with price data collected weekly, monthly, or half-yearly for specific items. Again, why should tobacco products prices be included in CPI calculations? Or we could debate of how the housing price index is calculated for inclusion in the CPI. Does age composition of the household matter in calculating the CPI basket? For a relatively young family, would the basket of goods not be different than those families with more elders than children?

So, to cut my long argument short, please refrain from creating indices that just simply represent a mish-mash of parameters to evaluate a start-up.

My recommendation: Use reference class forecasting

Reference class forecasting (RCF), sometimes also referred to as comparison class forecasting is a method recommended to overcome cognitive biases and misplaced incentives. My favourite article on this appeared in The McKinsey Quarterly (see here). Let me elaborate the theory first.

Nobel laureate Daniel Kahneman and Amos Tversky’s work on theories of decision making under uncertainty is the starting point for understanding RCF. They described how people make decisions that are seemingly irrational while dealing with probabilities and forecasts using Prospect Theory (see an insightful class by Prof. Schiller, another Nobel Laureate, on YouTube here). Summary relevant to us: people are more concerned by smaller losses than equivalent gains; and people round off probabilities of occurrence to either zero or one, when it is close to either, and in between, exaggerate.

Let us understand how an entrepreneur could use this theory to manipulate his capital provider. She shows some initial success, and likens her business model to an already successful model somewhere else, in some other context; and gets the investor to exaggerate the probability of her success. For example, I know a friend wanted to build the Uber of toys in India. Why buy toys, just rent them, let the child play for a week, and return it back to the library next week to issue a new set of toys. Sounds exciting? Just that the economics did not work out the cost of damages to the toys small children could do, that would render it useless for the next borrower (like breaking one car wheel). The entrepreneur kept the rentals high enough to account for such losses, and soon her customers realised that the rentals were working out far more expensive than buying new toys, notwithstanding the child refusing to part with his toys at the end of the week. The entrepreneur continued to convince his investors to keep investing in her, luring them to wait for the economies of scale to kick-in and she could have enough bargaining power with toy manufacturers to directly import from the North of Himalayas, but that never happened and the investor exited the firm at its lowest valuation.

These biases manifest themselves in the form of delusional optimism, rather than a clear understanding and detailed evaluation of costs and benefits, even when hard data is available.

Steps in using RCF: A field guide

RCF helps forecasters and planners overcome these biases by situating the reference point outside of the subject being assessed. In order to forecast (or assess future performance) a business, investors need to identify a reference class of analogous businesses, estimate the distribution of the outcomes of those firms, and benchmark the enterprise at an appropriate point of the distribution. Firstly, the investors should identify appropriate reference class for the enterprise. These reference classes need to be identified using a variety of parameters that match the enterprise. The next step is to analyse the performance of the firms in the reference class and map them into a probability distribution. There may be clusters of firms that may emerge during this distribution-mapping exercise; there may be instances of only extremes of firm performance observed (say in winner-takes-all markets); or there could be continuous distributions.

The next task is to use pluralism in the parameters to position the enterprise in the distribution. Here is where multiple parameters would help in an reliable estimate of the position. For instance, an Uber for toys in India would only work when the marginal costs of renting out a car (wear and tear) is negligible compared to the fixed (sunk) costs of buying the car. Whereas in the toys market, the marginal costs of a child playing with the toy is a significant proportion of the market price of the toy, and therefore this enterprise would not be subject to the same evolutionary direction as Uber. However, if the enterprise was repositioned as a toy library (as my friend ultimately did), it would work – look at how the cost structures of library and toys work. It provided her a benchmark on only buying those toys that would be durable, held the customer’s attention for only short periods of time, and were very expensive to buy. Typical examples were multi-player games, which no child wanted to own independently (given the small size of families today), but would rent out during the weekends/ birthday parties for a small proportion of the cost of the game.

So, hers is calling entrepreneurs and investors to overcome such cognitive biases and forecast better.

Comments and feedback welcome.

 

%d bloggers like this: