How to rob a bank… without ever being caught..

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Marketing 101 – Hilarious and informative

Rory Sutherland on the importance of perception and perceived value.

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Performance improvements in sports over the years-Technology, Gene Pool, Mindset

Have you ever wondered why records in most seconds/meters/kilos-sports keep improving, David Epstein, author of “The Sports Gene“, explains why:thanks to changes in technology, gene pool and mindset.

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No room for Leadership in large organizations

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Do you have a “leadership team” in your organization ?

Chances are, if you work in a large organization, that there are many teams referring to themselves as “leadership teams”, and many individuals within these teams that see themselves as “leaders”.

Frankly, in my experince, the vast majority of these “leaders” and “leadership teams” have nothing whatsoever to do with Leadership – instead, what’s performed by these individuals and teams is a combination of management, control, administration and policing of predefined processes. .

“Leadership” to me implies things like inspiring, coaching, motivating, supporting, educating as well as running in front of the team, not only setting the pace and direction, but actively participating and contributing to the outcome of the team.

For Leadership to be meaningful, the organization can not be placed under and run by a rigorous set of detailed processes, which only leaves room for execution; in such organizations, what’s there left for the leader to ‘lead’…? for someone to act like a Leader, there need to be latitude to maneouver outside of a rigid process and governance structure, there must exist possibility and authority for the leader to take decisions of her own, not just following and monitoring execution of predefined processes.

Leadership demands competent and experienced human professionals.  Many of today’s large organizations have killed all possibilities for leadership to exist  at all in the organization, by creating a governance  and process structure so rigid and detailed that it leaves absolutely no room for any actions outside of the structure. Such an organization could equally well be populated by – as well as ‘led’ by – human automatons, i.e. robots, that will flawlessly execute the processes they have been programmed to perform, without any need for Leadership.

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What’s it like being a techie surrounded by empty suits….?

Unfortunately not that unrealistic description of life as a technical expert… 🙂

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Agile Embedded Software Development

Andrea Tomasini of Agile42 busting some myths.

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“Five lessons about leadership learned as a sergeant in IDF

Great insights into true leadership from Dan Schoenbaum.  (my comments inside brackets)

  • “You have to earn it!” – [i.e. you can only become a true leader if you prove yourself worthy to lead, there’s no room for empty suits – a signum for a meritocracy]
  • “Teaming to take that hill” – [a great example of what Steve Denning refers to as “Mission Command” (asop to “Detailed Command”)]
  • “Leaders need to be generalists, not specialists” – [beware of the simple mind MBA’s who after 4 years in school “knows everything”]
  • “Get your hands dirty” – [if the troops are cold, crowded and hungry, so should the leader be – avoid the perks of 1st class life style]
  • “Get knocked down 5 times, get up 6” – [(occasional) failure should not only be accepted, but expected for learning and growth]
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MH370 and Bayesian reasoning: example of SAR optimization

In two previous posts, [1,2] I’ve covered the fundamentals of Bayesian probability theory.  The second post looked into how the air distaster investigation team might have proceeded, once they received Inmarsat’s satellite data, to assess the likelihood of the flight path taken by MH370.

The post builds upon the second post above, taking a look how Bayesian reasoning could be used to assign priorities to different and disjunct search areas.

Let’s assume we are looking for a missing person, ship or aircraft, and furthermore that we have identified three all inclusive and mutually exhausive areas, that is, the target must reside in one of these areas.

For each of these areas, we assign an initial (‘prior’) probability for the target to be located within that area.  For each area, we also assign a specific search team, each such team having an effectiveness factor that indicates how thoroughly the team will be able to search their area. For instance, an aircraft doing only visual surveillance of the area is able to cover a lot of ground quickly, but its effectiveness, i.e. ability to locate the target with high certainty is low, compared to e.g. a team equipped with search dogs.

The table below shows how the search is initially planned: there are three fully inclusive, mutually exclusive search areas A, B, C,  each with an a priori assigned probability for target being there, and each with an effectiveness factor of the search team:

 
Area          A          B          C
Prior P       5%         70%        25%
Factor E      0          0.8        0.3

Multiplying the P with the E for each of the areas gives us 
the initial target detection probability for each area:

P(detect)     0%         56%        8%

Let's assume that we only have two search teams available, 
so area A is left without search activity in the first round.

Now, either one of the two search teams will locate the target, 
and in that case, the job is done. If however the target remains 
undetected, the search coordinator, after talking to the teams, 
might assign other teams with other capabilities, and thus 
effectiveness factors to the different areas.  
Let's say that for the second round of search, the new areas are 
assigned teams as follows (the search of area B is intensified):

Area          A          B          C
Prior P       5%         70%        25%
Factor E      0          0.9        0.3

Now, let's assume that also for this second round, the search is unsuccessful. 

Now, by applying Bayesian reasoning, the search coordinator can 
start drawing some conclusions: let's take area B as an example: 
the prior for B is 70%, but we have already searched that area 
with a relatively high effectiveness factor of 0.9. That should 
indicate that the probability for the target to reside in B has 
decreased after the searches already performed. Similarily, 
for area C, we have only invested a modest search effectiveness, 
and although our initial prior for C was moderate, now, after the 
initial searches, we will revise our beliefs using Bayes: to do that, 
for each area we now multiply the prior with the complement 
effectiveness, that is, P x (1 - E), which gives us the probability 
for the target remaining in the unsearched parts of the area. 
As the final step, we the divide each such probability with the sum 
of all three probabilities, which gives us a revised probability for 
each area for the target to reside there. 

Applying the numbers above, gives us the following revised (posterior) 
target location probability:

Area           A          B          C
Posterior P   16%         24%        60%

Since our initial assumption was the three areas are fully inclusive 
(but mutually exclusive), the sum of the probabilities must be 100%. 

As can be seen, based on these numbers, the search coordinator should 
now switch priority from area B to area C for continued searches, 
assigning the most effective team to area C. 


 

 

 

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How to ruin a great team

An interesting post from the world of soccer, where the Manchester United have taken  a radical fall after Alex Ferguson left

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Bayesian reasoning and SAR for flight MH370

I covered the basics of Bayesian reasoning in a previous post.

So, let’s apply Bayesian reasoning to the search & rescue operation of flight MH370. As we now know, Inmarsat’s identification of the most likely path taken by MH370, by means of Doppler analysis, proved significant in identifying the most likely flightpath taken.

In this post, I’m speculating on how the crash investigation team might have used Inmarsat’s data:

When MH370 disappeared from ATC radar, it could have taken any course from its latest known position. Although some courses would rationally be much more likely than others – e.g. based on an assumption that the aircraft would continue flying towards its destination – let’s for this exercise assume that each one degree course of the compass is equally probable, and that we for some reason settle on a southerly course as our assumption. There goes 360 degrees to a complete circle, so our prior probability for a southerly course is thus 1/360, about 0.28%.

Then, Inmarsat finds this ‘strange’ Doppler effect indicating that some aircraft has been flying a southerly course in the relevant window of time. Let’s assume that their analysis of a multitude of similar Doppler tracks for a multitude of different flights eventually reveal a probability of 95% of the Doppler track giving an accurate description of the actual flight track, and a 5 procent rate of ‘false positive’, that is, a Doppler track showing southerly course without an actual flight following that path.

Thus, from a Bayesian perspective, our “Prior”, that is, our initial best guess for the course taken by MH370 has a very low probability of 0.28%, but with the new evidence, provided by Inmarsat’s Doppler analysis, we are able to get a revised probability.

Plugging in these numbers into the Bayesian formula described in the previous post, all of sudden, with the new Doppler analysis evidence provided by Inmarsat, our assumption that the flight proceeded at a specific (180 deg) southerly course, has the probability of 51%!

That is, by applying Bayesian analysis to the problem, we were able (in this hypotetical example) to narrow down our S&R options from a quarter of a percentage to more than 50%, for a single degree of search sector.

Should we increase the width of the search sector, from its current 1 deg, we get the posterior probabilities as follows (everything else being constant):

Search sector width in degrees:        Posterior probability:
1 degree                                    51%
2 degrees                                   68%
3 degrees                                   76%
4 degrees                                   81%
5 degrees                                   84%

Obviously, by increasing the search sector size, we get a larger area to search, but as can be seen from the numbers above, the probability for the target residing in the selected area increases rapidly with the width of the sector, even for these fairly narrow sectors.

 

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