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Sensemaking and Analytics for Organizations

The worth of an education – a metalogue

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To be prepared against surprise is to be trained. To be prepared for surprise is to be educated.” – James Carse in Finite and Infinite Games.

Prospective student: How much do your students earn after they graduate?

Teacher: I’m sorry, I don’t know.

Prospective student: What do you mean? Don’t you keep track of what students do after they complete the program?

Teacher: Yes, we do, but we don’t ask them how much they earn. It is not the kind of question one asks….

Prospective student:  I know that, but shouldn’t you be gathering hard data on outcomes?

Teacher:  Of course, and we gather a range of data including the roles they get on graduation and how they progress in their careers.

Prospective student:  OK, but that doesn’t help me. How do I know that your degree is worth the hefty fees you charge?  If I’m investing that much, I must be sure of a decent return.

Teacher:  One cannot be certain about anything in life, there are never any guarantees. However, it’s perfectly reasonable to expect value from your education, so let me ask you – what would you consider a decent return?

Prospective student:   Hmm, a good job, I guess.

Teacher: Well, you heard from some of our alumni this evening.  They are currently employed as professionals in the field, and most of them got their jobs while studying or soon after completing their degree.

Prospective student: Yes, but you are unlikely to invite those who would say negative things about the program or those who have failed to get jobs.

Teacher: Fair enough, there are a few of those.

Prospective student: Well, that’s just my point. How do I know it will work for me?

Teacher:  You don’t!  Getting a job is an indirect effect of a good education.

Prospective student: I’m not sure I follow.

Teacher: An analogy might help clarify what I mean by indirect, Bill Gates did not become a multi-billionaire by setting out to become one. He became one by following his interests, his passion.

Prospective student: [a tad irritated] I still don’t get it.

Teacher: The objective of education is not to train you for a vocation…although, if you do things right, you are almost certain to get a job, and a good one at that. The aim of education is personal transformation, to broaden your perspective and thus enable you to look anew at the things you do at work and, possibly, even in life. Another aim is to prepare you to become that buzzword: a lifelong learner. No university can teach you those things, but they can help you learn them.

Prospective student: “personal transformation” sounds wonderful but terribly vague. Could you give me an example?

Teacher: Well, you’ve just heard from a few of our alumni and I could tell you many more stories. The thing is, I don’t think it is helpful to hear second-hand stories – their journeys are theirs, not yours. The story I’m interested in right now is yours: what you’re thinking, what you do and where you want to go.

Prospective student: OK. I’m a financial analyst [Editor’s note: feel free to substitute your current profession here] and I want to be a data scientist. Will this course enable me to become one?

Teacher: It could, but whether or not it actually does depends largely on you.

Prospective student: That’s not an answer.

Teacher: It is, and it’s an honest one. No course will make you a data scientist. And if any university tells you they can, they’re lying. What a good university course will do is help you learn the technical and non-technical skills that will enable you to become a data scientist.  Whether you learn or not depends on you. The responsibility for your personal transformation lies largely with you. All we can do is show you the way.

Prospective student: so, do you cover … [student recites a litany of data science languages and techniques].

Teacher: Yes, we cover them.

Prospective student:  Won’t doing those make me a data scientist?

Teacher: No. If tech skills are all you are after, I’d strongly suggest you don’t join our program…or any other university program for that matter. Instead, head off to one of the good online data science education providers and save yourself a whole lot of money.

Prospective student: Huh?

Teacher:  A good face-to-face program at a university covers a whole lot more than tech. For example, there are certain tacit skills and dispositions that are critical to becoming a good data scientist. These skills have to do with problem finding rather than technical adeptness or problem solving.  A good university course will give you opportunities to gain experience in doing that.

Prospective student; Problem finding? What’s that?

Teacher: In university assignments you’re given readymade problems that you can go off and solve. In real life, however, you are rarely given a problem. More often, you are presented with a situation from which you must extract or formulate a problem before you solve it. That’s not always a straightforward process because every situation is unique in its details.

Prospective student: If every situation is unique then there is no formula to deal with it.

Teacher: Exactly! These skills have to be taught indirectly – by putting students in safe-to-fail situations in which they can learn how to deal with the ambiguity inherent in them

Prospective student: But won’t that be throwing students into a situation they’re unprepared for.

Teacher: Although they may not admit it, most consultants – even experienced ones – rarely feel totally in control when dealing with new clients. It’s good to experience that kind of ambiguity early in one’s career, even if it is a second career. Every consulting engagement is a learning experience. This ties in with what I mentioned earlier – becoming a lifelong learner.

Prospective student: So how do you prepare students to deal with these types of scenarios?

Teacher: Through carefully crafted technical and non-technical subjects, with assignments that make them think rather than just do. To do well in the assignments you will have to think things through, try different approaches and even make judgement calls.

Prospective student: Judgement calls?

Teacher:  Yes, that’s right. You will find that the biggest issues when doing data science in the real world are not technical, rather they are about dealing with ambiguous situations in which you don’t have a well-defined problem or adequate data.  Then there are ethical issues that are becoming ever more important today. There are big corporations that completely ignore the ethical implications of what they do. Just because you can do something, it doesn’t mean you should.  All these issues involve judgement calls in which data is of little or no help.

Prospective student: Hmm, I didn’t realise there were so many facets to being a data scientist. Thanks, you’ve given something to think about.

Teacher: No worries…and good luck, I hope you find what you’re looking for.




HR gurus and consultants continually pontificate about the future of work. The ground reality for many mid-career professionals is that the future of their work is highly uncertain, much of the uncertainty being fuelled by the perception that data-related technologies are going to “disrupt” established industries. Among other things, this has led to an unprecedented demand for courses that teach data-related skills.

What is often left unsaid, however, is the transition to data science – or any profession for that matter – involves more than just picking up technical skills. The biggest missing piece (in my opinion) is the ability to make sense of ambiguous situations. This is a tacit skill that is difficult, if not impossible, to teach but can be learnt given the right environment and attitude. The university ought to provide the environment, the student the attitude.

Note: A metalogue is a dialogue that unfolds in such a way that the structure of the conversation turns out to be illustrative of the issue being discussed. The anthropologist Gregory Bateson coined the term.  Here is a metalogue written by him.

Written by K

February 18, 2020 at 5:40 am

Posted in Metalogues, Organizations, sensemaking

Tagged with

Complex decision making as an infinite game

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A decision is the act of choosing between two or more options.

There are two kinds of decisions, computable and non-computable [1]. In the former, options are well-defined and finite in number, and there are unambiguous facts (data) available based on which options can be rated. In the latter, options are neither clear nor enumerable and facts, if available at all, are ambiguous.

Computable decisions are simple, non-computable decisions are complex. We’ll refer to the two decision types by these names in the remainder of this article.

An example of a simple decision is buying a product (TV, car or whatever) based on well-defined criteria (price, features etc.). An example of a complex decision is formulating a business strategy.

It should be clear that simple decisions involve smaller temporal and monetary stakes – i.e. the cost of getting things wrong is limited and the effects of a bad decision wear off in (a relatively short) time. Neither is true for complex decisions: the cost of a poor choice can be significant, and its negative effects tend to persist over time.

A key feature of complex decisions is that they (usually) affect multiple parties. That is, they are socially complex. This has implications regarding how such decisions should be approached. More on this later.

Conventional decision theory is based on the notion of maximizing benefit or utility. For simple decisions it is assumed that utility of each option can be computed; for complex decisions it is assumed they can be estimated, or at least ranked. The latter assumption is questionable because each party affected by a complex decision will have its own notion of utility, at least at the outset. Moreover, since neither options nor facts are unambiguous at the start, it makes little sense to attempt to estimate utility upfront.

The above being the case, it is clear that complex decisions cannot be made on the basis of maximizing utility alone.  Something else is needed.


James Carse’s classic book, Finite and Infinite Games, begins with the following lines:

There are at least two kinds of games. One could be called finite, the other infinite. A finite game is played for the purpose of winning, an infinite for the purpose of continuing the play.

A finite game ends when a player or team wins. However, “just as it is essential for a finite game to have a definitive ending, it must also have a precise beginning. Therefore, we can speak of finite games as having temporal boundaries.”

The parallel between simple decisions and finite games should be evident. Although less obvious, it is useful to think of a complex decision as an infinite game.

When making a complex decision – such as a business strategy – decision-makers will often focus on maximising potential benefits (aka utility). However, as often as not, the outcome of the decision will fall far short of the expected benefits and may, in some cases, even lead to ruin. This being so, it is perhaps more fruitful to focus on staying in the game (keep playing) rather than winning (maximising utility).

The aim of a complex decision should be to stay in the game rather than win.

How does one ensure that one stays in the game? Heinz von Foerster’s ethical imperative offers an answer”

Always act to increase your choices.

That is, one should decide in such a way that increases one’s options in the future thereby increasing chances of staying in the game. One can frame this discussion in terms of adaptability:  the greater the number of options the greater the ability to adapt to unexpected changes in the environment.

How can one “act to increase one’s choices”?

One way to do this is to leverage social complexity: get different parties to articulate their preferred options. Some of these options are likely to contradict each other. Nevertheless, there are ways to handle such a diversity of potentially contradictory views in an inclusive manner (for an example, see this paper; for more, check out this book). Such an approach also ensures that the problem and solution spaces are explored more exhaustively than if only a limited number of viewpoints are considered.


The point is this: there are always more options available than apparent. Indeed, the number of unexplored options at any stage is potentially infinite. The job of the infinite player (decision-maker) is to act so as surface them gradually, and thus stay in the game.


Traditionally, decision-making is seen as a logical undertaking based on facts or data. In contrast, when viewed as an infinite game, complex decision-making becomes a matter of ethics rather than logic.

Why ethics?

The answer lies in von Foerster’s dictum to increase one’s choices.  By doing so, one increases the chances that fewer stakeholders’ interests are overlooked in the decision-making process.

As Wittgenstein famously said, “It is clear ethics cannot be articulated.” All those tedious classes and books on business ethics miss the point entirely. Ethical matters are necessarily oblique:  the decision-maker who decides in a way that increases (future) choices, will be acting ethically without drawing attention to it, or even being consciously aware of it.


Any honest discussion of complex decision-making in organisations must address the issue of power.

Carse asserts that players (i.e. decision-makers in the context of this article) become powerful by acquiring titles (e.g. CEO, Manager etc.). However, such titles can only be acquired by winning a finite game– i.e. by being successful in competitions for roles. Power therefore relates to finite rather than infinite games.

As he notes in his book:

Power is a concept that belongs only in finite play. To speak meaningfully of a person’s power is to speak of what that person has already achieved, the titles they have already won.

Be that as it may, one cannot overlook the reality that those in powerful positions can (and often do) subvert the decision-making process by obstructing open and honest discussion of contentious issues. Sometimes they do so by their mere presence in the room.

How does a complex decision-maker deal with the issue of power?

Carse offers the following answer:

How do infinite players contend with power? Since the outcome of infinite play is endlessly open, there is no way of looking back to make an assessment of the power or weakness of earlier play. Infinite players look forward, not to a victory but toward ongoing play. A finite player plays to be powerful; the infinite player plays with strength. Power is concerned (and a consequence of) what has happened, strength with what has yet to happen. Power will be always restricted to a relatively small number of people. Anyone can be strong.

What strength means is context-dependent, but the following may help clarify its relationship to power:

Late last year I attended an end-of-year event at the university I teach at. There I bumped into a student I had mentored some time ago. We got talking about his workplace (a large government agency).

At one point he asked, “We really need to radically change the way we think about and work with data, but I’m not a manager and have no authority to initiate changes that need to be made.”

“Why don’t you demonstrate what you are capable of? Since you are familiar your data, it should be easy enough to frame and tackle a small yet meaningful data science problem.” I replied.

“What if my manager doesn’t like my taking the initiative?”

“It is easier to beg forgiveness than seek permission.”

“He might feel threatened and make life difficult for me.”

“If management doesn’t like you’re doing, it’s their loss. What’s the worst that could happen? You could lose your job. With what you are learning at university you should have no trouble moving on to another role. Indeed, by doing so, you will diversify your experience and increase your future options.”


To summarise:  when deciding on complex matters, act in a way that maximises possibility rather than utility. Such an approach is inherently ethical and enhances one’s chances of staying in the game.

Complex decision making is an infinite game.

[1] There are many other terms for this classification:  tame and wicked (Horst Rittel), programmed and non-programmed (Herbert Simon), complicated and complex (David Snowden). Paul Culmsee and I have, perhaps confusingly, used the terms uncertain and ambiguous to refer to these in our books.  There are minor contextual differences between how these different authors interpret these terms, but for the most part they are synonymous with computable/non-computable.


Written by K

January 21, 2020 at 4:09 am

Tackling the John Smith Problem – deduplicating data via fuzzy matching in R

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Last week I attended a CRM  & data user group meeting for not-for-profits (NFPs), organized by my friend Yael Wasserman from Mission Australia. Following a presentation from a vendor, we broke up into groups and discussed common data quality issues that NFPs (and dare I say most other organisations) face. Number one on the list was the vexing issue of duplicate constituent (donor) records – henceforth referred to as dupes. I like to call this the John Smith Problem as it is likely that a typical customer database in a country with a large Anglo population is likely to have a fair number of records for customers with that name.  The problem is tricky because one has to identify John Smiths who appear to be distinct in the database but are actually the same person, while also ensuring that one does not inadvertently merge two distinct John Smiths.

The John Smith problem is particularly acute for NFPs as much of their customer data comes in either via manual data entry or bulk loads with less than optimal validation. To be sure, all the NFPs represented at the meeting have some level of validation on both modes of entry, but all participants admitted that dupes tend to sneak in nonetheless…and at volumes that merit serious attention.  Yael and his team have had some success in cracking the dupe problem using SQL-based matching of a combination of fields such as first name, last name and address or first name, last name and phone number and so on. However, as he pointed out, this method is limited because:

  1. It does not allow for typos and misspellings.
  2. Matching on too few fields runs the risk of false positives – i.e. labelling non-dupes as dupes.

The problems arise because SQL-based matching requires  one to pre-specify match patterns. The solution is straightforward: use fuzzy matching instead. The idea behind fuzzy matching is simple:  allow for inexact matches, assigning each match a similarity score ranging from 0 to 1 with 0 being complete dissimilarity and 1 being a perfect match. My primary objective in this article is to show how one can make headway with the John Smith problem using the fuzzy matching capabilities available in R.

A bit about fuzzy matching

Before getting down to fuzzy matching, it is worth a brief introduction on how it works. The basic idea is simple: one has to generalise the notion of a match from a binary “match” / “no match” to allow for partial matching. To do this, we need to introduce the notion of an edit distance, which is essentially the minimum number of operations required to transform one string into another. For example, the edit distance between the strings boy and bay is 1: there’s only one edit required to transform one string to the other. The Levenshtein distance is the most commonly used edit distance. It is essentially, “the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.”

A variant called the  Damerau-Levenshtein distance, which additionally allows for the transposition of two adjacent characters (counted as one operation, not two) is found to be more useful in practice.  We’ll use an implementation of this called the optimal string alignment (osa) distance. If you’re interested in finding out more about osa, check out the Damerau-Levenshtein article linked to earlier in this paragraph.

Since longer strings will potentially have larger numeric distances between them, it makes sense to normalise the distance to a value lying between 0 and 1. We’ll do this by dividing the calculated osa distance by the length of the larger of the two strings . Yes, this is crude but, as you will see, it works reasonably well. The resulting number is a normalised measure of the dissimilarity between the two strings. To get a similarity measure we simply subtract the dissimilarity from 1. So, a normalised dissimilarity of 1 translates to similarity score of 0 – i.e. the strings are perfectly dissimilar.  I hope I’m not belabouring the point; I just want to make sure it is perfectly clear before going on.


In what follows, I assume you have R and RStudio installed. If not, you can access the software here and here for Windows and here for Macs; installation for both products is usually quite straightforward.

You may also want to download the Excel file many_john_smiths which contains records for ten fictitious John Smiths. At this point I should affirm that as far as the dataset is concerned, any resemblance to actual John Smiths, living or dead, is purely coincidental! Once you have downloaded the file you will want to open it in Excel and examine the records and save it as a csv file in your R working directory (or any other convenient place)  for processing in R.

As an aside, if you have access to a database, you may also want to load the file into a table called many_john_smiths and run the following dupe-detecting SQL statement:

select * from many_john_smiths t1
where exists
(select 'x' from many_john_smiths t2
t1.CustomerID <> t2.CustomerID)

You may also want to try matching on other column combinations such as First/Last Name and AddressLine1 or First/Last Name and AddressSuburb for example. The limitations of column-based exact matching will be evident immediately. Indeed,  I have deliberately designed the records to highlight some of the issues associated with dirty data: misspellings, typos, misheard names over the phone etc. A quick perusal of the records will show that there are probably two distinct John Smiths in the list. The problem is to quantify this observation. We do that next.

Tackling the John Smith problem using R

We’ll use the following libraries: stringdist and stringi . The first library, stringdist, contains a bunch of string distance functions, we’ll use stringdistmatrix() which returns a matrix of pairwise string distances (osa by default) when  passed a vector of strings, and stringi has a number of string utilities from which we’ll use str_length(), which returns the length of string.

OK, so on to the code. The first step is to load the required libraries:

#load libraries

We then read in the data, ensuring that we override the annoying default behaviour of R, which is to convert strings to categorical variables – we want strings to remain strings!

#read data, taking care to ensure that strings remain strings
df <- read.csv("many_john_smiths.csv",stringsAsFactors = F)
#examine dataframe

The output from str(df) (not shown)  indicates that all columns barring CustomerID are indeed strings (i.e. type=character).

The next step is to find the length of each row:

#find length of string formed by each row (excluding title)
rowlen <- str_length(paste0(df$FirstName,df$LastName,df$AddressLine1,
#examine row lengths
> [1] 41 43 39 42 28 41 42 42 42 43

Note that I have excluded the Title column as I did not think it was relevant to determining duplicates.

Next we find the distance between every pair of records in the dataset. We’ll use the stringdistmatrix()function mentioned earlier:

#stringdistmatrix - finds pairwise osa distance between every pair of elements in a
#character vector
d <- stringdistmatrix(paste0(df$FirstName,df$LastName,df$AddressLine1,
1 2 3 4 5 6 7 8 9
2 7
3 10 13
4 15 21 24
5 19 26 26 15
6 22 21 28 12 18
7 20 23 26 9 21 14
8 10 13 17 20 23 25 22
9 19 22 19 21 24 29 23 22
10 17 22 25 13 22 19 16 22 24

stringdistmatrix() returns an object of type dist (distance), which is essentially a vector of pairwise distances.

For reasons that will become clear later, it is convenient to normalise the distance – i.e. scale it to a number that lies between 0 and 1. We’ll do this by dividing the distance between two strings by the length of the longer string. We’ll use the nifty base R function combn() to compute the maximum length for every pair of strings:

#find the length of the longer of two strings in each pair
pwmax <- combn(rowlen,2,max,simplify = T)

The first argument is the vector from which combinations are to be generated, the second is the group size (2, since we want pairs) and the third argument indicates whether or not the result should be returned as an array (simplify=T) or list (simplify=F). The returned object, pwmax, is a one-dimensional array containing the pairwise maximum lengths. This has the same length and is organised in the same way as the object d returned by stringdistmatrix() (check that!). Therefore, to normalise d we simply divide it by pwmax

#normalised distance
dist_norm <- d/pwmax

The normalised distance lies between 0 and 1 (check this!) so we can define similarity as 1 minus distance:

#similarity = 1 - distance
similarity <- round(1-dist_norm,2)
sim_matrix <- as.matrix(similarity)
1 2 3 4 5 6 7 8 9 10
1 0.00 0.84 0.76 0.64 0.54 0.46 0.52 0.76 0.55 0.60
2 0.84 0.00 0.70 0.51 0.40 0.51 0.47 0.70 0.49 0.49
3 0.76 0.70 0.00 0.43 0.33 0.32 0.38 0.60 0.55 0.42
4 0.64 0.51 0.43 0.00 0.64 0.71 0.79 0.52 0.50 0.70
5 0.54 0.40 0.33 0.64 0.00 0.56 0.50 0.45 0.43 0.49
6 0.46 0.51 0.32 0.71 0.56 0.00 0.67 0.40 0.31 0.56
7 0.52 0.47 0.38 0.79 0.50 0.67 0.00 0.48 0.45 0.63
8 0.76 0.70 0.60 0.52 0.45 0.40 0.48 0.00 0.48 0.49
9 0.55 0.49 0.55 0.50 0.43 0.31 0.45 0.48 0.00 0.44
10 0.60 0.49 0.42 0.70 0.49 0.56 0.63 0.49 0.44 0.00

The diagonal entries are 0, but that doesn’t matter because we know that every string is perfectly similar to itself! Apart from that, the similarity matrix looks quite reasonable: you can, for example, see that records 1 and 2 (similarity score=0.84) are quite similar while records 1 and 6 are quite dissimilar (similarity score=0.46).  Now let’s extract some results more systematically. We’ll do this by printing out the top 5 non-diagonal similarity scores and the associated records for each of them. This needs a bit of work. To start with, we note that the similarity matrix (like the distance matrix) is symmetric so we’ll convert it into an upper triangular matrix to avoid double counting. We’ll also set the diagonal entries to 0 to avoid comparing a record with itself:

#convert to upper triangular to prevent double counting
sim_matrix[lower.tri(sim_matrix)] <- 0
#set diagonals to zero to avoid comparing row to itself
diag(sim_matrix) <- 0

Next we create a function that returns the n largest similarity scores and their associated row and column number – we’ll need the latter to identify the pair of records that are associated with each score:

#adapted from:
nlargest <- function(m, n) {
res <- order(m, decreasing = T)[seq_len(n)];
pos <- arrayInd(res, dim(m), useNames = TRUE);
list(values = m[res],
position = pos)

The function takes two arguments: a matrix m and a number n indicating the top n scores to be returned. Let’s set this number to 5 – i.e. we want the top 5 scores and the associated record indexes. We’ll store the output of nlargest in the variable sim_list:

top_n <- 5
sim_list <- nlargest(sim_matrix,top_n)

Finally, we loop through sim_list printing out the scores and associated records as we go along:

for (i in 1:top_n){
rec <- as.character(df[sim_list$position[i],])
sim_rec <- as.character(df[sim_list$position[i+top_n],])
cat("score: ",sim_list$values[i],"\n")
cat("record 1: ",rec,"\n")
cat ("record 2: ",sim_rec,"\n\n")
score: 0.84
record 1: 1 John Smith Mr 12 Acadia Rd Burnton 9671 1234 5678
record 2: 2 Jhon Smith Mr 12 Arcadia Road Bernton 967 1233 5678

score: 0.79
record 1: 4 John Smith Mr 13 Kynaston Rd Burnton 9671 34561234
record 2: 7 Jon Smith Mr. 13 Kinaston Rd Barnston 9761 36451223

score: 0.76
record 1: 1 John Smith Mr 12 Acadia Rd Burnton 9671 1234 5678
record 2: 3 J Smith Mr. 12 Acadia Ave Burnton 867`1 1233 567

score: 0.76
record 1: 1 John Smith Mr 12 Acadia Rd Burnton 9671 1234 5678
record 2: 8 John Smith Dr 12 Aracadia St Brenton 9761 12345666

score: 0.71
record 1: 4 John Smith Mr 13 Kynaston Rd Burnton 9671 34561234
record 2: 6 John S Dr. 12 Kinaston Road Bernton 9677 34561223

As you can see, the method correctly identifies close matches: there appear to be 2 distinct records (1 and 4) – and possibly more, depending on where one sets the similarity threshold. I’ll leave you to explore this further on your own.

The John Smith problem in real life

As a proof of concept, I ran the following SQL on a real CRM database hosted on SQL Server:

group by
order by
count(*) desc

I was gratified to note that John Smith did indeed come up tops – well over 200 records. I suspected there were a few duplicates lurking within, so I extracted the records and ran the above R code (with a few minor changes). I found there indeed were some duplicates! I also observed that the code ran with no noticeable degradation despite the dataset having well over 10 times the number of records used in the toy example above. I have not run it for larger datasets yet, but I suspect one will run into memory issues when the number of records gets into the thousands. Nevertheless, based on my experimentation thus far, this method appears viable for small datasets.

The problem of deduplicating large datasets is left as an exercise for motivated readers 😛

Wrapping up

Often organisations will turn to specialist consultancies to fix data quality issues only to find that their work, besides being quite pricey, comes with a lot of caveats and cosmetic fixes that do not address the problem fully.  Given this, there is a case to be made for doing as much of the exploratory groundwork as one can so that one gets a good idea of what can be done and what cannot. At the very least, one will then be able to keep one’s consultants on their toes. In my experience, the John Smith problem ranks right up there in the list of data quality issues that NFPs and many other organisations face. This article is intended as a starting point to address this issue using an easily available and cost effective technology.

Finally,  I should reiterate that the approach discussed here is just one of many possible and is neither optimal nor efficient.  Nevertheless, it works quite well on small datasets, and is therefore offered here as a starting point for your own attempts at tackling the problem. If you come up with something better – as I am sure you can – I’d greatly appreciate your letting me know via the contact page on this blog or better yet, a comment.


I’m indebted to Homan Zhao and Sree Acharath for helpful conversations on fuzzy matching.  I’m also grateful to  all those who attended the NFP CRM and Data User Group meetup that was held earlier this month – the discussions at that meeting inspired this piece.

Written by K

October 9, 2019 at 8:49 pm

Posted in Data Analytics, Data Science, R

Tagged with

3 or 7, truth or trust

with one comment

“It is clear that ethics cannot be articulated.” – Ludwig Wittgenstein

Over the last few years I’ve been teaching and refining a series of lecture-workshops on Decision Making Under Uncertainty. Audiences include data scientists and mid-level managers working in corporates and public service agencies. The course is based on the distinction between uncertainties in which the variables are known and can be quantified versus those in which the variables are not known upfront and/or are hard to quantify.

Before going any further, it is worth explaining the distinction via a couple of examples:

An example of the first type of uncertainty is project estimation. A project has an associated time and cost, and although we don’t know what their values are upfront, we can estimate them if we have the right data.  The point to note is this: because such problems can be quantified, the human brain tends to deal with them in a logical manner.

In contrast, business strategy is an example of the second kind of uncertainty. Here we do not know what the key variables are upfront. Indeed we cannot, because different stakeholders will perceive different aspects of a strategy to be paramount depending on their interests – consider, for example, the perspective of a CFO versus that of a CMO. Because of these differences, one cannot make progress on such problems until agreement has been reached on what is important to the group as a whole.  The point to note here is that since such problems involve contentious issues, our reactions to them  tend to be emotional rather than logical.

The difference between the two types of uncertainty is best conveyed experientially, so I have a few in-class activities aimed at doing just that. One of them is an exercise I call “3 or 7“, in which I give students a sheet with the following printed on it:

Circle either the number 3 or 7 below depending on whether you want 3 marks or 7 marks added to your Assignment 2 final mark. Yes, this offer is for real, but there a catch: if more than 10% of the class select 7, no one gets anything.

Write your student ID on the paper so that Kailash can award you the marks. Needless to say, your choice will remain confidential, no one (but Kailash) will know what you have selected.

3              7

Prior to handing out the sheet, I tell them that they:

  • should sit far enough apart so that they can’t see what their neighbours choose,
  • are not allowed to communicate their choices to others until the entire class has turned their sheets.

Before reading any further you may want to think about what typically happens.


Many readers would have recognized this exercise as a version of the Prisoner’s Dilemma and, indeed, many students in my classes recognize this too.   Even so, there are always enough of “win at the cost of others” types in the room who ensure that I don’t have to award any extra marks. I’ve run the exercise about 10 times, often with groups comprised of highly collaborative individuals who work well together. Despite that,15-20% of the class ends up opting for 7.

It never fails to surprise me that, even in relatively close-knit groups, there are invariably a number of individuals who, if given a chance to gain at the expense of their colleagues, will not hesitate to do so providing their anonymity is ensured.


Conventional management thinking deems that any organisational activity involving several people has to be closely supervised. Underlying this view is the assumption that individuals involved in the activity will, if left unsupervised, make decisions based on self-interest rather than the common good (as happens in the prisoner’s dilemma game). This assumption finds justification in rational choice theory, which predicts that individuals will act in ways that maximise their personal benefit without any regard to the common good. This view is exemplified in 3 or 7 and, at a societal level, in the so-called Tragedy of the Commons, where individuals who have access to a common resource over-exploit it,  thus depleting the resource entirely.

Fortunately, such a scenario need not come to pass: the work of Elinor Ostrom, one of the 2009 Nobel prize winners for Economics, shows that, given the right conditions, groups can work towards the common good even if it means forgoing personal gains.

Classical economics assumes that individuals’ actions are driven by rational self-interest – i.e. the well-known “what’s in it for me” factor. Clearly, the group will achieve much better results as a whole if it were to exploit the resource in a cooperative way. There are several real-world examples where such cooperative behaviour has been successful in achieving outcomes for the common good (this paper touches on some). However, according to classical economic theory, such cooperative behaviour is simply not possible.

So the question is: what’s wrong with rational choice theory?  A couple of things, at least:

Firstly, implicit in rational choice theory is the assumption that individuals can figure out the best choice in any given situation.  This is obviously incorrect. As Ostrom has stated in one of her papers:

Because individuals are boundedly rational, they do not calculate a complete set of strategies for every situation they face. Few situations in life generate information about all potential actions that one can take, all outcomes that can be obtained, and all strategies that others can take.

Instead, they use heuristics (experienced-based methods), norms (value-based techniques) and rules (mutually agreed regulations) to arrive at “good enough” decisions.  Note that Ostrom makes a distinction between norms and rules, the former being implicit (unstated) rules, which are determined by the cultural attitudes and values)

Secondly, rational choice theory assumes that humans behave as self-centred, short-term maximisers. Such theories work in competitive situations such as the stock-market but not in situations in which collective action is called for, such as the prisoners dilemma.

Ostrom’s work essentially addresses the limitations of rational choice theory by outlining how individuals can work together to overcome self-interest.


In a paper entitled, A Behavioral Approach to the Rational Choice Theory of Collective Action, published in 1998, Ostrom states that:

…much of our current public policy analysis is based on an assumption that rational individuals are helplessly trapped in social dilemmas from which they cannot extract themselves without inducement or sanctions applied from the outside. Many policies based on this assumption have been subject to major failure and have exacerbated the very problems they were intended to ameliorate. Policies based on the assumptions that individuals can learn how to devise well-tailored rules and cooperate conditionally when they participate in the design of institutions affecting them are more successful in the field…[Note:  see this book by Baland and Platteau, for example]

Since rational choice theory aims to maximise individual gain,  it does not work in situations that demand collective action – and Ostrom presents some very general evidence to back this claim.  More interesting than the refutation of rational choice theory, though, is Ostrom’s discussion of the ways in which individuals “trapped” in social dilemmas end up making the right choices. In particular she singles out two empirically grounded ways in which individuals work towards outcomes that are much better than those offered by rational choice theory. These are:

Communication: In the rational view, communication makes no difference to the outcome.  That is, even if individuals make promises and commitments to each other (through communication), they will invariably break these for the sake of personal gain …or so the theory goes. In real life, however, it has been found that opportunities for communication significantly raise the cooperation rate in collective efforts (see this paper abstract or this one, for example). Moreover, research shows that face-to-face is far superior to any other form of communication, and that the main benefit achieved through communication is exchanging mutual commitment (“I promise to do this if you’ll promise to do that”) and increasing trust between individuals. It is interesting that the main role of communication is to enhance or reinforce the relationship between individuals rather than to transfer information.  This is in line with the interactional theory of communication.

Innovative Governance:  Communication by itself may not be enough; there must be consequences for those who break promises and commitments. Accordingly, cooperation can be encouraged by implementing mutually accepted rules for individual conduct, and imposing sanctions on those who violate them. This effectively amounts to designing and implementing novel governance structures for the activity. Note that this must be done by the group; rules thrust upon the group by an external authority are unlikely to work.

Of course, these factors do not come into play in artificially constrained and time-bound scenarios like 3 or 7.  In such situations, there is no opportunity or time to communicate or set up governance structures. What is clear, even from the simple 3 or 7 exercise,  is that these are required even for groups that appear to be close-knit.

Ostrom also identifies three core relationships that promote cooperation. These are:

Reciprocity: this refers to a family of strategies that are based on the expectation that people will respond to each other in kind – i.e. that they will do unto others as others do unto them.  In group situations, reciprocity can be a very effective means to promote and sustain cooperative behaviour.

Reputation: This refers to the general view of others towards a person. As such, reputation is a part of how others perceive a person, so it forms a part of the identity of the person in question. In situations demanding collective action, people might make judgements on a person’s reliability and trustworthiness based on his or her reputation.’

Trust: Trust refers to expectations regarding others’ responses in situations where one has to act before others. And if you think about it, everything else in Ostrom’s framework is ultimately aimed at engendering or – if that doesn’t work – enforcing trust.


In an article on ethics and second-order cybernetics, Heinz von Foerster tells the following story:

I have a dear friend who grew up in Marrakech. The house of his family stood on the street that divide the Jewish and the Arabic quarter. As a boy he played with all the others, listened to what they thought and said, and learned of their fundamentally different views. When I asked him once, “Who was right?” he said, “They are both right.”

“But this cannot be,” I argued from an Aristotelian platform, “Only one of them can have the truth!”

“The problem is not truth,” he answered, “The problem is trust.”

For me, that last line summarises the lesson implicit in the admittedly artificial scenario of 3 or 7. In our search for facts and decision-making frameworks we forget the simple truth that in many real-life dilemmas they matter less than we think. Facts and  frameworks cannot help us decide on ambiguous matters in which the outcome depends on what other people do.  In such cases the problem is not truth; the problem is trust.  From your own experience it should be evident it is impossible convince others of your trustworthiness by assertion, the only way to do so is by behaving in a trustworthy way. That is, by behaving ethically rather than talking about it, a point that is squarely missed by so-called business ethics classes.

Yes,  it is clear that ethics cannot be articulated.


  1. Portions of this article are lightly edited sections from a 2009 article that I wrote on Ostrom’s work and its relevance to project management.
  2.  Finally, an unrelated but important matter for which I seek your support for a common good: I’m taking on the 7 Bridges Walk to help those affected by cancer. Please donate via my 7 Bridges fundraising page if you can . Every dollar counts; all funds raised will help Cancer Council work towards the vision of a cancer free future.

Written by K

September 18, 2019 at 8:28 pm

The Turing Inversion – an #AI fiction

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…Since people have to continually understand the uncertain, ambiguous, noisy speech of others, it seems they must be using something like probabilistic reasoning…” – Peter Norvig

“It seems that the discourse of nonverbal communication is precisely concerned with matters of relationship – love, hate, respect, fear, dependency, etc.—between self and others or between self and environment, and that the nature of human society is such that falsification of this discourse rapidly becomes pathogenic” – Gregory Bateson


It was what, my fourth…no, fifth visit in as many weeks; I’d been there so often, I felt like a veteran employee who’s been around forever and a year. I wasn’t complaining, but it was kind of ironic that the selection process for a company that claimed to be about “AI with heart and soul” could be so without either. I’d persisted because it offered me the best chance in years of escaping from the soul-grinding environs of a second-rate university. There’s not much call outside of academia for anthropologists with a smattering of tech expertise, so when I saw the ad that described me to a T, I didn’t hesitate.

So there I was for the nth time in n weeks.

They’d told me they really liked me, but needed to talk to me one last time before making a decision. It would be an informal chat, they said, no technical stuff. They wanted to understand what I thought, what made me tick. No, no psychometrics they’d assured me, just a conversation. With whom they didn’t say, but I had a sense my interlocutor would be one of their latest experimental models.


She was staring at the screen with a frown of intense concentration, fingers drumming a complex tattoo on the table. Ever since the early successes of Duplex with its duplicitous “um”s and “uh”s, engineers had learnt that imitating human quirks was half the trick to general AI.  No intelligence required, only imitation.

I knocked on the open door gently.

She looked up, frown dissolving into a smile. Rising from her chair, she extended a hand in greeting. “Hi, you must be Carlos, she said.  I’m Stella. Thanks for coming in for another chat. We really do appreciate it.”

She was disconcertingly human.

“Yes, I’m Carlos. Good to meet you Stella,” I said, mustering a professional smile.  “Thanks for the invitation.”

“Please take a seat. Would you like some coffee or tea?”

“No thanks.” I sat down opposite her.

“Let me bring up your file before we start,” she said, fingers dancing over her keyboard.  “Incidentally, have you read the information sheet HR sent you?”

“Yes, I have.”

“Do you have any questions about the role or today’s chat?”

“No I don’t at the moment, but may have a few as our conversation proceeds.”

“Of course,” she said, flashing that smile again.

Much of the early conversation was standard interview fare: work history, what I was doing in my current role and how it was relevant to the job I had applied for etc.  Though she was impressively fluent, her responses were were well within the capabilities of the current state of art. Smile notwithstanding, I reckoned she was probably an AI.

Then she asked, “as an anthropologist, how do you think humans will react to AIs that are conversationally indistinguishable from humans?”

“We are talking about a hypothetical future,” I replied warily, “…we haven’t got to the point of indistinguishability yet.”


“Well… yes…at least for now.”

“OK, if you say so,” she said enigmatically, “let’s assume you’re right and treat that as a question about a ‘hypothetical’ future AI.”

“Hmm, that’s a difficult one, but let me try…most approaches to conversational AI work by figuring out an appropriate response using statistical methods. So, yes, assuming the hypothetical AI has a vast repository of prior conversations and appropriate algorithms, it could – in principle – be able to converse flawlessly.” It was best to parrot the party line, this was an AI company after all.

She was having none of that. “I hear the ‘but’ in your tone,” she said, “why don’t you tell me what you really think?”

“….Well there’s much more to human communication than words,” I replied, “more to conversations than what’s said. Humans use non-verbal cues such as changes in tone or facial expressions and gestures…”

“Oh, that’s a solved problem,” she interrupted with a dismissive gesture, “we’ve come a very long way since the primitive fakery of Duplex.”

“Possibly, but there’s more. As you probably well know, much of human conversation is about expressing emotions and…”

“…and you think AIs will not be able to do that?” she queried, looking at me squarely, daring me to disagree.

I was rattled but could not afford to show it. “Although it may be possible to design conversational AIs that appear to display emotion via, say, changes in tone, they won’t actually experience those emotions,” I replied evenly.

“Who is to say what another experiences? An AI that sounds irritated, may actually be irritated,” she retorted, sounding more than a little irritated herself.

“I’m not sure I can accept that,” I replied, “A machine may learn to display the external manifestation of a human emotion, but it cannot actually experience the emotion in the same way a human does. It is simply not wired to do that.”

“What if the wiring could be worked in?”

“It’s not so simple and we are a long way from achieving that, besides…”

“…but it could be done in principle” she interjected.

“Possibly, but I don’t see the point of it.  Surely…”

“I’m sorry” she said vehemently, “I find your attitude incomprehensible. Why should machines not be able display, or indeed, even experience emotions?  If we were talking about humans, you would be accused of bias!”

Whoa, a de-escalation was in order. “I’m sorry,” I said, “I did not mean to offend.”

She smiled that smile again. “OK, let’s leave the contentious issue of emotion aside and go back to the communicative aspect of language. Would you agree that AIs are close to achieving near parity with humans in verbal communication?”

“Perhaps, but only in simple, transactional conversations,” I said, after a brief pause. “Complex discussions – like say a meeting to discuss a business strategy – are another matter altogether.”


“Well, transactional conversations are solely about conveying information. However, more complex conversations – particularly those involving people with different views – are more about building relationships. In such situations, it is more important to focus on building trust than conveying information. It is not just a matter of stating what one perceives to be correct or true because the facts themselves are contested.”

“Hmm, maybe so, but such conversations are the exception not the norm. Most human exchanges are transactional.”

“Not so. In most human interactions, non-verbal signals like tone and body language matter more than words. Indeed, it is possible to say something in a way that makes it clear that one actually means the opposite. This is particularly true with emotions. For example, if my spouse asks me how I am and I reply ‘I’m fine’ in a tired voice, I make it pretty clear that I’m anything but.  Or when a boy tells a girl that he loves her, she’d do well to pay more attention to his tone and gestures than his words. The logician’s dream that humans will communicate unambiguously through language is not likely to be fulfilled.” I stopped abruptly, realising I’d strayed into contentious territory again.

“As I recall Gregory Bateson alluded to that in one of his pieces,” she responded, that disconcerting smile again.

“Indeed he did! I’m impressed that you made the connection.”

“No you aren’t,” she said, smile tightening, “It was obvious from the start that you thought I was an AI, and an AI would make the connection in a flash.”

She had taken offence again. I stammered an apology which she accepted with apparent grace.

The rest of the conversation was a blur; so unsettled was I by then.


“It’s been a fascinating conversation, Carlos,” she said, as she walked me out of the office.

“Thanks for your time,” I replied, “and my apologies again for any offence caused.”

“No offence taken,” she said, “it is part of the process. We’ll be in touch shortly.” She waved goodbye and turned away.

Silicon or sentient, I was no longer sure. What mattered, though, was not what I thought of her but what she thought of me.



  1. Norvig, P., 2017. On Chomsky and the two cultures of statistical learning. In Berechenbarkeit der Welt? (pp. 61-83). Springer VS, Wiesbaden. Available online at: http://norvig.com/chomsky.html
  2.  Bateson, G., 1968. Redundancy and coding. Animal communication: Techniques of study and results of research, pp.614-626. Reprinted in Steps to an ecology of mind: Collected essays in anthropology, psychiatry, evolution, and epistemology. University of Chicago Press, 2000, p, 418.

Written by K

May 1, 2019 at 10:13 pm

Posted in AI Fiction

Seven Bridges revisited – further reflections on the map and the territory

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The  Seven Bridges Walk is an annual fitness and fund-raising event organised by the Cancer Council of New South Wales. The picturesque 28 km circuit weaves its way through a number of waterfront suburbs around Sydney Harbour and takes in some spectacular views along the way.  My friend John and I did the walk for the first time in 2017. Apart from thoroughly enjoying the experience, there was  another, somewhat unexpected payoff: the walk evoked some thoughts on project management and the map-territory relationship which I subsequently wrote up in a post on this blog.

Figure 1:The map, the plan

We enjoyed the walk so much that we decided to do it again in 2018. Now, it is a truism that one cannot travel exactly the same road twice. However, much is made of the repeatability of certain kinds of experiences. For example, the discipline of project management is largely predicated on the assumption that projects are repeatable.  I thought it would be interesting to see how this plays out in the case of a walk along a well-defined route, not the least because it is in many ways akin to a repeatable project.

To begin with, it is easy enough to compare the weather conditions on the two days: 29 Oct 2017 and 28 Oct 2018. A quick browse of this site gave me the data as I was after (Figure 2).

Figure 2: Weather on 29 Oct 2017 and 28 Oct 2018

The data supports our subjective experience of the two walks. The conditions in 2017 were less than ideal for walking: clear and uncomfortably warm with a hot breeze from the north.  2018 was considerably better: cool and overcast with a gusty south wind – in other words, perfect walking weather. Indeed, one of the things we commented on the second time around was how much more pleasant it was.

But although weather conditions matter, they tell but a part of the story.

On the first walk, I took a number of photographs at various points along the way. I thought it would be interesting to take photographs at the same spots, at roughly the same time as I did the last time around, and compare how things looked a year on. In the next few paragraphs I show a few of these side by side (2017 left, 2018 right) along with some comments.

We started from Hunters Hill at about 7:45 am as we did on our first foray, and took our first photographs at Fig Tree Bridge, about a kilometre from the starting point.

Figure 3: Lane Cove River from Fig Tree Bridge (2017 Left, 2018 Right)

The purple Jacaranda that captivated us in 2017 looks considerably less attractive the second time around (Figure 3): the tree is yet to flower and what little there is there does not show well in the cloud-diffused light. Moreover, the scaffolding and roof covers on the building make for a much less attractive picture. Indeed, had the scene looked so the first time around, it is unlikely we would have considered it worthy of a photograph.

The next shot (Figure 4), taken not more than a  hundred metres from the previous one, also looks considerably different:  rougher waters and no kayakers in the foreground. Too cold and windy, perhaps?  The weather and wind data in Fig 2 would seem to support that conclusion.

Figure 4: Morning kayakers on the river (2017 Left, 2018 Right)

The photographs in Figure 5 were taken at Pyrmont Bridge  about four hours into the walk. We already know from Figure 4 that it was considerably windier in 2018. A comparison of the flags in the two shots in Figure 5 reveal an additional detail: the wind was from opposite directions in the two years. This is confirmed by the weather information in Figure 2, which also tells us that the wind was from the north in 2017 and the south the following year (which explains the cooler conditions).  We can even get an  approximate temperature: the photographs were taken around 11:30 am both years, and a quick look at Figure 2 reveals that the temperature at noon was about 30 C in 2017 and 18 C in 2018.

Figure 5: Pyrmont Bridge (2017 Left, 2018 Right)

The point about the wind direction and cloud conditions is also confirmed by comparing the photographs in Figure 6, taken at Anzac Bridge, a few kilometres further along the way (see the direction of the flag atop the pylon).

Figure 6: View looking up Anzac Bridge (2017 L, 2018 R)

Skipping over to the final section of the walk, here are a couple of shots I took towards the end: Figure 7 shows a view from Gladesville Bridge and Figure 8 shows one from Tarban Creek Bridge.  Taken together the two confirm some of the things we’ve already noted regarding the weather and conditions for photography.

Figure 7: View from Gladesville Bridge (2017 L, 2018 R)

Further, if you look closely at Figures 7 and 8, you will also see the differences in the flowering stage of the Jacaranda.

Figure 8: View from Tarban Creek Bridge (2017 L, 2018 R)

A detail that I did not notice until John pointed it out is that the the boat at the bottom edge of  both photographs in Fig. 8 is the same one (note the colour of the furled sail)! This was surprising to us, but it should not have been so.  It turns out that boat owners have to apply for private mooring licenses and are allocated positions at which they install a suitable mooring apparatus. Although this is common knowledge for boat owners, it likely isn’t so for others.

The photographs are a visual record of some of the things we encountered  along the way. However, the details in recorded in them have more to do with aesthetics rather the experience – in photography of this kind, one tends to preference what looks good over what happened. Sure, some of the photographs offer hints about the experience but much of this is incidental and indirect. For example,  when taking the photographs in Figures 5 and 6, it was certainly not my intention to record the wind direction. Indeed, that would have been a highly convoluted way to convey information that is directly and more accurately described by the data in Figure 2 . That said, even data has limitations: it can help fill in details such as the wind direction and temperature but it does not evoke any sense of what it was like to be there, to experience the experience, so to speak.

Neither data nor photographs are the stuff memories are made of. For that one must look elsewhere.


As Heraclitus famously said, one can never step into the same river twice. So it is with walks.  Every experience of a walk is unique; although map remains the same the territory is invariably different on each traverse, even if only subtly so. Indeed, one could say that the territory is defined through one’s experience of it. That experience is not reproducible, there are always differences in the details.

As John Salvatier points out, reality has a surprising amount of detail, much of which we miss because we look but do not see. Seeing entails a deliberate focus on minutiae such as the play of morning light on the river or tree; the cool damp from last night’s rain; changes in the built environment, some obvious, others less so.  Walks are made memorable by precisely such details, but paradoxically  these can be hard to record in a meaningful way.  Factual (aka data-driven) descriptions end up being laundry lists that inevitably miss the things that make the experience memorable.

Poets do a better job. Consider, for instance, Tennyson‘s take on a brook:

“…I chatter over stony ways,
In little sharps and trebles,
I bubble into eddying bays,
I babble on the pebbles.

With many a curve my banks I fret
By many a field and fallow,
And many a fairy foreland set
With willow-weed and mallow.

I chatter, chatter, as I flow
To join the brimming river,
For men may come and men may go,
But I go on for ever….”

One can almost see and hear a brook. Not Tennyson’s, but one’s own version of it.

Evocative descriptions aren’t the preserve of poets alone. Consider the following description of Sydney Harbour, taken from DH Lawrence‘s Kangaroo:

“…He took himself off to the gardens to eat his custard apple-a pudding inside a knobbly green skin-and to relax into the magic ease of the afternoon. The warm sun, the big, blue harbour with its hidden bays, the palm trees, the ferry steamers sliding flatly, the perky birds, the inevitable shabby-looking, loafing sort of men strolling across the green slopes, past the red poinsettia bush, under the big flame-tree, under the blue, blue sky-Australian Sydney with a magic like sleep, like sweet, soft sleep-a vast, endless, sun-hot, afternoon sleep with the world a mirage. He could taste it all in the soft, sweet, creamy custard apple. A wonderful sweet place to drift in….”

Written in 1923, it remains a brilliant evocation of the Harbour even today.

Tennyson’s brook and Lawrence’s Sydney do a better job than photographs or factual description, even though the latter are considered more accurate and objective. Why?  It is because their words are more than mere description: they are stories that convey a sense of what it is like to be there.


The two editions of the walk covered exactly the same route, but our experiences of the territory on the two instances were very different. The differences were in details that ultimately added up to the uniqueness of each experience.  These details cannot  be captured by maps and visual or written records, even in principle. So although one may gain familiarity with certain aspects of a territory through repetition, each lived experience of it will be unique. Moreover, no two individuals will experience the territory in exactly the same way.

When bidding for projects, consultancies make much of their prior experience of doing similar projects elsewhere. The truth, however, is that although two projects may look identical on paper they will invariably be different in practice.  The map,  as Korzybski famously said, is not the territory.  Even more, every encounter with the territory is different.

All this is not to say that maps (or plans or data) are useless, one needs them as orienting devices. However, one must accept that they offer limited guidance on how to deal with the day-to-day events and occurrences on a project. These tend to be unique because they are highly context dependent. The lived experience of a project is therefore necessarily different from the planned one. How can one gain insight into the former? Tennyson and Lawrence offer a hint: look to the stories told by people who have traversed the territory, rather than the maps, plans and data-driven reports they produce.

Written by K

February 15, 2019 at 8:24 am

Posted in Project Management

Another distant horizon

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It was with a sense of foreboding that I reached for my phone that Sunday morning. I had spoken with him the day before and although he did not say, I could sense he was tired.

“See you in a few weeks,” he said as he signed off, “and I’m especially looking forward to seeing the boys.”

It was not to be. Twelve hours later, a missed call from my brother Kedar and the message:

“Dad passed away about an hour or so ago…”

The rest of the day passed in a blur of travel arrangements and Things that Had to Be Done Before Leaving. My dear wife eased my way through the day.

I flew out that evening.


A difficult journey home. I’m on a plane, surrounded by strangers. I wonder how many of them are making the journey for similar reasons.

I turn to the inflight entertainment. It does not help. Switching to classical music, I drift in and out of a restless sleep.

About an hour later, I awaken to the sombre tones of Mozart’s Requiem, I cover my head with the blanket and shed a tear silently in the dark.


Monday morning, Mumbai airport, the waiting taxi and the long final leg home.

Six hours later, in the early evening I arrive to see Kedar, waiting for me on the steps, just as Dad used to.

I hug my Mum, pale but composed. She smiles and enquires about my journey.  “I’m so happy to see you,” she says.

She has never worn white, and does not intend to start now. “Pass me something colourful,” she requests the night nurse, “I want to celebrate his life, not mourn his passing.”


The week in Vinchurni is a blur of visitors, many of whom have travelled from afar. I’m immensely grateful for the stories they share about my father, deeply touched that many of them consider him a father too.

Mum ensures she meets everyone, putting them at ease when the words don’t come easily. It is so hard to find the words to mourn another’s loss. She guides them – and herself – through the rituals of condolence with a grace that seem effortless. I know it is not.


Some days later, I sit in his study and the memories start to flow…

A Skype call on my 50th Birthday.

“Many Happy Returns,” he booms, “…and remember, life begins at 50.”

He knew that from his own experience: as noted in this tribute written on his 90th birthday, his best work was done after he retired from the Navy at the ripe young age of 54.

A conversation in Vinchurni, may be twenty years ago. We are talking about politics, the state of India and the world in general. Dad sums up the issue brilliantly:

“The problem,” he says, “is that we celebrate the mediocre. We live in an age of mediocrity.”

Years earlier, I’m faced with a difficult choice. I’m leaning one way, Dad recommends the other.

At one point in the conversation he says, “Son, it’s your choice but I think you are situating your appreciation instead of appreciating the situation.”

He had the uncanny knack of finding the words to get others to reconsider their ill-considered choices.

Five-year-old me on a long walk with Dad and Kedar. We are at a lake in the Nilgiri Hills, where I spent much of my childhood. We collect wood and brew tea on an improvised three-stone fire. Dad’s beard is singed brown as he blows on the kindling. Kedar and I think it’s hilarious and can’t stop laughing.  He laughs with us.


 “I have many irons in the fire,” he used to say, “and they all tend to heat up at the same time.”

It made for a hectic, fast-paced life in which he achieved a lot by attempting so much more.

This photograph sums up how I will always remember him, striding purposefully towards another distant horizon.


Written by K

January 14, 2019 at 8:05 pm

Posted in Personal

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