Archive for the 'Getting-things-done intelligence' Category

High Velocity Decision-Making

Amazon’s Jeff Bezos on decision making, in his 2016 Annual Letter to shareholders:

Day 2 companies make high-quality decisions, but they make high-quality decisions slowly. To keep the energy and dynamism of Day 1, you have to somehow make high-quality, high-velocity decisions. Easy for start-ups and very challenging for large organizations. The senior team at Amazon is determined to keep our decision-making velocity high. Speed matters in business – plus a high-velocity decision making environment is more fun too. We don’t know all the answers, but here are some thoughts.

First, never use a one-size-fits-all decision-making process. Many decisions are reversible, two-way doors. Those decisions can use a light-weight process. For those, so what if you’re wrong? I wrote about this in more detail in last year’s letter.

Second, most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions. If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.

Third, use the phrase “disagree and commit.” This phrase will save a lot of time. If you have conviction on a particular direction even though there’s no consensus, it’s helpful to say, “Look, I know we disagree on this but will you gamble with me on it? Disagree and commit?” By the time you’re at this point, no one can know the answer for sure, and you’ll probably get a quick yes.

This isn’t one way. If you’re the boss, you should do this too. I disagree and commit all the time. We recently greenlit a particular Amazon Studios original. I told the team my view: debatable whether it would be interesting enough, complicated to produce, the business terms aren’t that good, and we have lots of other opportunities.They had a completely different opinion and wanted to go ahead. I wrote back right away with “I disagree and commit and hope it becomes the most watched thing we’ve ever made.” Consider how much slower this decision cycle would have been if the team had actually had to convince me rather than simply get my commitment.

Note what this example is not: it’s not me thinking to myself “well, these guys are wrong and missing the point,but this isn’t worth me chasing.” It’s a genuine disagreement of opinion, a candid expression of my view, achance for the team to weigh my view, and a quick, sincere commitment to go their way. And given that this team has already brought home 11 Emmys, 6 Golden Globes, and 3 Oscars, I’m just glad they let me in the roomat all!

Fourth, recognize true misalignment issues early and escalate them immediately.  Sometimes teams have different objectives and fundamentally different views. They are not aligned. No amount of discussion, no number of meetings will resolve that deep misalignment. Without escalation, the default dispute resolution mechanism for this scenario is exhaustion. Whoever has more stamina carries the decision.

I’ve seen many examples of sincere misalignment at Amazon over the years. When we decided to invite third party sellers to compete directly against us on our own product detail pages – that was a big one. Many smart,well-intentioned Amazonians were simply not at all aligned with the direction. The big decision set up hundreds of smaller decisions, many of which needed to be escalated to the senior team.

“You’ve worn me down” is an awful decision-making process. It’s slow and de-energizing. Go for quick escalation instead – it’s better.

So, have you settled only for decision quality, or are you mindful of decision velocity too? Are the world’s trends tailwinds for you? Are you falling prey to proxies, or do they serve you? And most important of all, are you delighting customers? We can have the scope and capabilities of a large company and the spirit and heart of a small one. But we have to choose it.”




London life

 

FandW




The New World versus the Old

Propose to an Englishman any principle, or any instrument, however admirable, and you will observe that the whole effort of the English mind is directed to find a difficulty, defect, or an impossibility in it.  If you speak to him of a machine for peeling a potato, he will pronounce it impossible:  if you peel a potato with it before his eyes, he will declare it useless, because it will not slice a pineapple.  Impart the same principle or show the same machine to an American, or to one of our colonists, and you will observe that the whole effort of his mind is to find some new application of the principle, some new use for the instrument. ”

Charles Babbage, 1852, in a paper on taxation.  Cited on page 132 of Doron Swade [2000]:  The Cogwheel Brain:  Charles Babbage and the Quest to Build the First Computer. London, UK:  Little, Brown and Company.




The science of delegation

Most people, if they think about the topic at all, probably imagine computer science involves the programming of computers.  But what are computers?  In most cases, these are just machines of one form or another.  And what is programming?  Well, it is the issuing of instructions (“commands” in the programming jargon) for the machine to do something or other, or to achieve some state or other.   Thus, I view Computer Science as nothing more or less than the science of delegation.

When delegating a task to another person, we are likely to be more effective (as the delegator or commander) the more we know about the skills and capabilities and curent commitments and attitudes of that person (the delegatee or commandee).   So too with delegating to machines.   Accordingly, a large part of theoretical computer science is concerned with exploring the properties of machines, or rather, the deductive properties of mathematical models of machines.  Other parts of the discipline concern the properties of languages for commanding machines, including their meaning (their semantics) – this is programming language theory.  Because the vast majority of lines of program code nowadays are written by teams of programmers, not individuals, then much of computer science – part of the branch known as software engineering – is concerned with how to best organize and manage and evaluate the work of teams of people.   Because most machines are controlled by humans and act in concert for or with or to humans, then another, related branch of this science of delegation deals with the study of human-machine interactions.   In both these branches, computer science reveals itself to have a side which connects directly with the human and social sciences, something not true of the other sciences often grouped with Computer Science: pure mathematics, physics, or chemistry. 

And from its modern beginnings 70 years ago, computer science has been concerned with trying to automate whatever can be automated – in other words, with delegating the task of delegating.  This is the branch known as Artificial Intelligence.   We have intelligent machines which can command other machines, and manage and control them in the same way that humans could.   But not all bilateral relationships between machines are those of commander-and-subordinate.  More often, in distributed networks machines are peers of one another, intelligent and autonomous (to varying degrees).  Thus, commanding is useless – persuasion is what is needed for one intelligent machine to ensure that another machine does what the first desires.  And so, as one would expect in a science of delegation, computational argumentation arises as an important area of study.

 




Suddenly, the fog lifts . . .

Andrew Wiles, prover of Wiles’ Theorem (aka Fermat’s Last Theorem), on the doing of mathematics:

Perhaps I could best describe my experience of doing mathematics in terms of entering a dark mansion. One goes into the first room, and it’s dark, completely dark. One stumbles around bumping into the furniture, and gradually, you learn where each piece of furniture is, and finally, after six months or so, you find the light switch. You turn it on, and suddenly, it’s all illuminated. You can see exactly where you were.

This describes my experience, over shorter time-frames, in studying pure mathematics as an undergraduate, with each new topic covered: epsilon-delta arguments in analysis; point-set topology; axiomatic set theory; functional analysis; measure theory; group theory; algebraic topology; category theory; statistical decision theory; integral geometry; etc.    A very similar process happens in learning a new language, whether a natural (human) language or a programming language.     Likewise, similar words describe the experience of entering a new organization (either as an employee or as a management consultant), and trying to understand how the organization works, who has the real power, what are the social relationships and dynamics within the organization, etc, something I have previously described here.

One encounters a new discipline or social organization, one studies it and thinks about it from as many angles and perspectives as one can, and eventually, if one is clever and diligent, or just lucky, a light goes on and all is illuminated.    Like visiting a new city and learning its layout by walking through it, frequently getting lost and finding one’s way again,  enlightenment requires work.  Over time, one learns not to be afraid in encountering a new subject, but rather to relish the state of inchoateness and confusion in the period between starting and enlightenment.  The pleasure and wonder of the enlightenment is so great, that it all the prior pain is forgotten.

Reference:

Andrew Wiles [1996],  speaking in Fermat’s Last Theorem, a BBC documentary by S. Singh and John Lynch: Horizon, BBC 1996,  cited in Frans Oort [2011 ]:  Did earlier thoughts inspire Grothendieck? (Hat tip).




Digital aspen forests

Brian Arthur has an article about automated and intelligent machine-to-machine communications creating a second digital economy underlying the first physical one, in the latest issue of The McKinsey Quarterly here.

I want to argue that something deep is going on with information technology, something that goes well beyond the use of computers, social media, and commerce on the Internet. Business processes that once took place among human beings are now being executed electronically. They are taking place in an unseen domain that is strictly digital. On the surface, this shift doesn’t seem particularly consequential—it’s almost something we take for granted. But I believe it is causing a revolution no less important and dramatic than that of the railroads. It is quietly creating a second economy, a digital one.

. . . .

We do have sophisticated machines, but in the place of personal automation (robots) we have a collective automation. Underneath the physical economy, with its physical people and physical tasks, lies a second economy that is automatic and neurally intelligent, with no upper limit to its buildout. The prosperity we enjoy and the difficulties with jobs would not have surprised Keynes, but the means of achieving that prosperity would have.

This second economy that is silently forming—vast, interconnected, and extraordinarily productive—is creating for us a new economic world. How we will fare in this world, how we will adapt to it, how we will profit from it and share its benefits, is very much up to us.”

Reference:

W. Brian Arthur [2011]:  The Second EconomyThe McKinsey Quarterly, October 2011.




Reliable Knowledge

How little scientists know who only know science!  Thanks again to Norm, I learn about some statements by a retired professor of chemistry, Peter Atkins, about how we know what we know.   Atkins is quoted as saying:

The scientific method is the only reliable method of achieving knowledge.”

Well, first, it is worth saying that the scientific method does not produce reliable knowledge.  One of the two defining features of science is that scientific claims are defeasible:  they may be contested, questioned, challenged, and even overthrown, if the evidence warrants.   There is nothing inherently reliable about any scientific claim or theory, since new evidence may be found at any time to overthrow it.  The history of science is littered with examples.   (The second key feature is that anyone may do this contesting; science is not, or rather should  not be, a priesthood.)

Continue reading ‘Reliable Knowledge’




Red River

One of my favourite films is Howard Hawks’ Red River (1948), which pitted John Wayne against Montgomery Clift.   I came across an insightful review of the movie by Roderick Heath, here. The one aspect of the movie not mentioned in that review is the context in which the movie was made, immediately after World War II.    At the time, the allies had large military forces being demobilized, with men – they were mostly men – returning with all deliberate speed to civilian life.  Many of these men had played responsible and important roles in the war effort, roles requiring intelligence, personal initiative, courage, and the leadership of others.  They returned to Civvy Street to find senior management posts occupied by the generation before them, and only subordinate roles available for themselves; they were often immensely frustrated.  I once heard of a businessman’s club memorial dedicated To the Men Whose Sons had Given Their Lives in World War II, which sums up for me the self-regard of the elder of these two generations.

With this context in mind, I see Red River as a parable about the struggle between the two generations for the control of business and society in the post-war world.   Clift’s caring and listening leadership style resonated much more with returning military men than Wayne’s deaf and inflexible approach, as it does also in the film with Wayne’s cattle drovers.   In Japan and Germany, of course, the generation before had made a mess of things, and so there were greater opportunities in the post-war period for the next generation to take immediate charge.




On Getting Things Done

New York Times Op-Ed writer, David Brooks, has two superb articles about the skills needed to be a success in contemporary technological society, the skills I refer to as Getting-Things-Done IntelligenceOne is a short article in The New York Times (2011-01-17), reacting to the common, but wrong-headed, view that technical skill is all you need for success, and the other a long, fictional disquisition in The New Yorker (2011-01-17) on the social skills of successful people.  From the NYT article:

Practicing a piece of music for four hours requires focused attention, but it is nowhere near as cognitively demanding as a sleepover with 14-year-old girls. Managing status rivalries, negotiating group dynamics, understanding social norms, navigating the distinction between self and group — these and other social tests impose cognitive demands that blow away any intense tutoring session or a class at Yale.

Yet mastering these arduous skills is at the very essence of achievement. Most people work in groups. We do this because groups are much more efficient at solving problems than individuals (swimmers are often motivated to have their best times as part of relay teams, not in individual events). Moreover, the performance of a group does not correlate well with the average I.Q. of the group or even with the I.Q.’s of the smartest members.

Researchers at the Massachusetts Institute of Technology and Carnegie Mellon have found that groups have a high collective intelligence when members of a group are good at reading each others’ emotions — when they take turns speaking, when the inputs from each member are managed fluidly, when they detect each others’ inclinations and strengths.

Participating in a well-functioning group is really hard. It requires the ability to trust people outside your kinship circle, read intonations and moods, understand how the psychological pieces each person brings to the room can and cannot fit together.

This skill set is not taught formally, but it is imparted through arduous experiences. These are exactly the kinds of difficult experiences Chua shelters her children from by making them rush home to hit the homework table.”

These articles led me to ask exactly what is involved in reading a social situation?  Brooks mentions some of the relevant aspects, but not all.   To be effective, a manager needs to parse the social situation of the groups he or she must work with – those under, those over and peer groups to the side – to answer questions such as the following:

  • Who has power or influence over each group?  Is this exercised formally or informally?
  • What are the norms and practices of the group, both explicit and implicit, known and unconscious?
  • Who in the group is reliable as a witness?   Whose stories can be believed?
  • Who has agendas and what are these?
  • Who in the group is competent or capable or intelligent?  Whose promises to act can be relied upon?  Who, in contrast, needs to be monitored or managed closely?
  • What constraints does the group or its members operate under?  Can these be removed or side-stepped?
  • What motivates the members of the group?  Can or should these motivations be changed, or enhanced?
  • Who is open to new ideas, to change, to improvements?
  • What obstacles and objections will arise in response to proposals for change?  Who will raise these?  Will these objections be explicit or hidden?
  • Who will resist or oppose change?  In what ways? Who will exercise pocket vetos?

Parsing new social situations – ie, answering these questions in a specific situation – is not something done in a few moments.  It may take years of observation and participation to understand a new group in which one is an outsider.  People who are good at this may be able to parse the key features of a new social landscape within a few weeks or months, depending on the level of access they have, and the willingness of the group members to trust them.     Good management consultants, provided their sponsors are sufficiently senior, can often achieve an understanding within a few weeks.   Experience helps.

Needless to say, most academic research is pretty useless for these types of questions.  Management theory has either embarked on the reduce-and-quantify-and-replicate model of academic psychology, or else undertaken the narrative descriptions of successful organizations of most books by business gurus.   Narrative descriptions of failures would be far more useful.

The best training for being able to answer such questions – apart from experience of life – is the study of anthropology or literature:  Anthropology because it explores the social structures of other cultures and the factors within a single lifetime which influence these structures, and Literature because it explores the motivations and consequences of human actions and interactions.   The golden age of television drama we are currently fortunate to be witness to also provides good training for viewers in human motivations, actions and interactions.  It is no coincidence, in my view, that the British Empire was created and run by people mostly trained in Classics, with its twofold combination of the study of alien cultures and literatures, together with the analytical rigor and intellectual discipline acquired through the incremental learning of those difficult subjects, Latin and Ancient Greek languages.

UPDATE (2011-02-16): From Norm Scheiber’s profile of US Treasury Secretary Timothy Geithner in The New Republic (2011-02-10):

“Tim’s real strength … is that he’s really quick at reading the culture of any institutions,” says Leslie Lipschitz, a former Geithner deputy.

The profile also makes evident Geithner’s agonistic planning approach to policy – seeking to incorporate opposition and minority views into both policy formation processes and the resulting policies.




Distributed cognition

Some excerpts from an ethnographic study of the operations of a Wall Street financial trading firm, bearing on distributed cognition and joint-action planning:

This emphasis on cooperative interaction underscores that the cognitive tasks of the arbitrage trader are not those of some isolated contemplative, pondering mathematical equations and connected only to to a screen-world.  Cognition at International Securities is a distributed cognition.  The formulas of new trading patterns are formulated in association with other traders.  Truly innovative ideas, as one senior trader observed, are slowly developed through successions of discreet one-to-one conversations.

. . .

An idea is given form by trying it out, testing it on others, talking about it with the “math guys,” who, significantly, are not kept apart (as in some other trading rooms),  and discussing its technical intricacies with the programmers (also immediately present).”   (p. 265)

The trading room thus shows a particular instance of Castell’s paradox:  As more information flows through networked connectivity, the more important become the kinds of interactions grounded in a physical locale. New information technologies, Castells (2000) argues, create the possibility for social interaction without physical contiguity.  The downside is that such interactions can become repititive and programmed in advance.  Given this change, Castells argues that as distanced, purposeful, machine-like interactions multiply, the value of less-directd, spontaneous, and unexpected interactions that take place in physical contiguity will become greater (see also Thrift 1994; Brown and Duguid 2000; Grabhar 2002).  Thus, for example, as surgical techniques develop together with telecommunications technology, the surgeons who are intervening remotely on patients in distant locations are disproportionately clustering in two or three neighbourhoods of Manhattan where they can socialize with each other and learn about new techniques, etc.” (p. 266)

“One examplary passage from our field notes finds a senior trader formulating an arbitrageur’s version of Castell’s paradox:

“It’s hard to say what percentage of time people spend on the phone vs. talking to others in the room.   But I can tell you the more electronic the market goes, the more time people spend communicating with others inside the room.”  (p. 267)

Of the four statistical arbitrage robots, a senior trader observed:

“We don’t encourage the four traders in statistical arb to talk to each other.  They sit apart in the room.  The reason is that we have to keep diversity.  We could really hammered if the different robots would have the same P&L [profit and loss] patterns and the same risk profiles.”  (p. 283)

References:

Daniel Beunza and David Stark [2008]:  Tools of the trade:  the socio-technology of arbitrage in a Wall Street trading room.  In:  Trevor Pinch and Richard Swedborg (Editors):  Living in a Material World:  Economic Sociology Meets Science and Technology Studies. Cambridge, MA, USA: MIT Press.  Chapter 8, pp. 253-290.

M. Castells [1996]:  The Information Age:  Economy, Society and Culture. Blackwell, Second Edition.