Category: complexity

How to Deal with Stress

Following our previous post on ‘How You Should Start By Being Cheerful‘, this post by Leo Babauta tackles a related topic: ‘A Guide to Letting Go of Stress‘.

Leo Babuta reminds us what is the real reason for our stress: “Things are out of control, not orderly, not simple, full of interruptions and unplanned events, health problems and accidents, and things never go as we planned or imagined. But this is the way the world is — the stress comes not because the world is messy and chaotic, but because we desire it to be different than it is.”

This is a very important statement, and one that we tend to forget. Stress is generated by the difference between reality and expectations. Since there are quite many things we can’t change in reality, we need to manage our expectations and ideals (the alternative, to alter reality to align with our expectations, is not a sustainable solution).

Therefore the recommended practice is to put together the conditions for self-awareness of this misalignment between reality and expectations, and let go of that difference (more details in the post). And then, “Even in moments of chaos, you can be free, and even appreciate the beauty of the chaos.”

Let’s remember that stress is indeed a misalignment between a messy reality and inner hopes and expectations, and that the solution is to realign both by letting go.

Share

How Zero Interest Rates May Affect the Innovation Economy

This very interesting post ‘The Social Consequences of Zero Interest Rates‘ examines the possible long term impact of this situation on innovation and the economy, taking as a model Japan where this situation has been prevalent for a longer time than anywhere else.

The article shows that innovation has decreased significantly in Japan in the last decades, since the 1990s which mark the end of Japan post-war catch-up and development phase. “Innovation ultimately has a lot to do with time preference in economic terms. Real innovations often only pay off years later, which is why innovative companies have to be prepared for a long haul. Zero interest rates counteract the power of innovation, because they almost always go hand in hand with higher time preference.” At the same time, wages stagnate and part-time employment grows. According to the author, all this negative evolution could be associated with high public debt / low interest rates.

This approach is interesting, however I tend to observe on the contrary that faced with very low interest rates, clever money tends to look for other places with potential gains and innovative startups tend to be quite awash with money these days – raising funds has rarely been as easy. Money also tends to get invested in shares and other high risk investments (which explains the high levels of the share market). There are quite other factors at work in Japan that could explain decreased innovation, for example the rigidity of the labour market and the traditional industrial age employment approaches.

What is certain, is that low interest rates increase the price of assets and proportionately make it more difficult to acquire them on the basis of wages, decreasing the actual purchasing power of people and increasing inequality. However, the impact on innovation is not as obvious to me. What are your views?

Share

How the World is Having a Savings Glut and This Will Accelerate

We are currently in a situation of excess savings in developed countries and therefore unprecedented availability of private funds for all sorts of new ventures including the most risky innovations and fundamental research projects (for example see our post ‘How Fundamental Scientific and Technological Programs are Now Run in a Competitive Manner by Private Companies‘). This excellent post ‘The Global Savings Glut, a Modern Policy Failure‘ gives a useful explanation of this situation which pervades our economic system and has accelerated since the 2008 crisis.

“We are operating in a world where there is a massive excess of capital vs. productive places to put it. Which is why valuations on high quality assets able to absorb this savings is so high.” And thus, when there is some start-up that looks like it could become high quality, it instantly attracts capital.

The analysis is that while China became the manufacturing center of the world, it did not let its currency appreciate, and started generate high levels of local positives as well. With goods becoming ever cheaper, savings capability increased in developed countries. “Globalization ended in 2011 and no one adjusted. Export based policy and high savings rates reinforced each other even as globalization forces weakened starting in 2011.” And of course, the constant policy of very low interest rates. With “the amount of savings in Asia and Europe far bigger than the size of their domestic asset markets“, the money pours into the US markets, killing in the process all sorts of possible US monetary policy.

It seems that with the pandemics the current trend will rather accelerate with ever cheaper money being poured to the economy. This can only increase the savings glut, make valuable assets more expensive, but also continue to foster private capital being poured into all sorts of fundamental initiatives that were previously performed by governments.

Share

How Memory and History are Different

In this post ‘The Most Important Question: How do you Know?‘, Valeria Maltoni tackles an interesting and important subject: the difference between history and memory. An interesting topic at the time we live through a historical and momentous historical event – the first pandemics of modern times.

I like the distinction which is proposed, quoting Alessandro Barbero, a famous italian writer of historic novels:

“Memory is individual, it’s the point of view of one person. History is the understanding of what happened from all points of view.”

Valeria Maltoni expands on this: “History is important. Reality its complex. To understand what actually happened, a view of the events from above is critical. One of the ways to learn from the experiences of others is to get out of a personal point of view, widen the gaze. This is what history does. Because it’s the sum of all the things that happened to human beings, history answers the question: What really happened? Memory is important, but by its very nature is limiting. It takes into account only one point of view“.

We will all have our memories of the current historical moment, but history will be needed to complement our understanding. And it will probably take time before this history can be written by people not getting involved in petty political games and interpretations.

Share

How Company Culture Is Essential To Prevent Long-Term Catastrophes

This interesting Forbes article ‘Avoid A Company Catastrophe With A Culture-Focused Approach‘ explores the issues of inadequate company culture in terms of long-term catastrophic outcomes, taking the specific example of Boeing and other previous catastrophic failures and major accidents.

A common topic emerges which is the capability for the organization to properly consider divergent opinions. When looking at the changes that are needed within Boeing, the most difficult appears to be “intellectual inclusion — a willingness to actually listen to other people’s opinions. It’s a difficult change that most companies aren’t willing to make. When incorporated correctly, however, it’s very powerful.”

Many organisations I know tend to have a ‘shoot the messenger’ attitude and have tremendous difficulties addressing diverging opinions. However it is quite true that this is an essential capability, even more so today when the world proves unstable and ripe for disruption.

Both on the short term and in the long term, a healthy corporate culture is an essential investment to navigate the hurdles of an uncertain world. It should probably be much more the focus of attention of senior leadership when building a company meant to last.

Share

How AI Algorithms Now Get Generated by Natural Selection

In this breathtaking post ‘Google Engineers ‘Mutate’ AI to Make It Evolve Systems Faster Than We Can Code Them‘, latest developments of AI algorithm generation is described.

It does not yet look like it really works for advanced algorithms, but there are possibilities that very quickly algorithms will evolve that will produce novel solutions to certain simple problems such as image recognition. This is quite an exciting – and troubling – development. It was due to arrive though with the development of ‘genetic algorithms’ that simulate natural selection.

Of course the novelty is now to apply it to AI algorithms which are by themselves heavier and more cumbersome to handle. Still it gives quite an interesting perspective of what we can expect in the short and medium term. Scary!

Share

How to Ensure Data Is Not a New Toxic Waste

There are quite a number of discussions about the ambiguous status of data in the new Collaborative Age. On one side it is celebrated as the new oil (refer to our post How Data Really is the New Oil, and Better); on the other side some argue that it is rather a toxic waste as in this interesting column ‘Data – the new oil, or potential for a toxic oil spill?‘

The point of the article is linked to data security and the harm that can be done through data theft and possible advanced recombination with other data sources that would also have been stolen. With zillions of data generated everyday, the argument is that one day or the other, sensitive data will leak and produce toxic effects on the wider data landscape and digital environment.

Specifically, the article mentions “Re-identification of anonymized data-sets [which] is a hot research topic for computer science today” and the fact that the breaches are additive in nature, progressively weakening privacy and sensitive data.

Of course, unclean data (refer to our post on data hygiene) is also another issue of toxic waste that may influence the wider data ecosystem if it is used as a basis for AI algorithm teaching or other reference applications.

The large amounts of data available today are a great source of value and at the same time are fraught with risks – as any new technology. Which will win first? My optimistic self is rather confident that the benefits will outweigh the risks, but that does not detract from the need to reinforce security and privacy.

Let’s make sure data is the source of value and not a toxic waste.

Share

How Navigation Apps Are Changing Municipal Traffic Policies

This post ‘Navigation Apps Changed the Politics of Traffic‘ explains how municipalities and local governments are adapting to the rise of navigation apps.

The issue is that those apps cater for the needs of the drivers, and not for the needs of the wider community. “Driver-first traffic “fixes,” even with the best of intentions, have deleterious effects on transportation networks overall.” There is a collective price of having each driver optimise its own route. “One widely cited 2001 paper by computer scientists at Cornell found that a network of “user-optimized” drivers can experience travel times equivalent to what a network of “system-optimized” drivers would experience with twice as many cars. Transport engineers call the difference between selfish and social equilibria the “price of anarchy.””

There seem to be some debate on the actual effect of those apps, and whether the algorithms also include some more collective constraints (one can remark for example that on two different phone, they may not give the same itinerary, showing that they try to spread congestion).

In any case as apps encourage the usage of smaller roads not normally used for transit, local governments act in restricting speed and transit possibilities in those smaller roads normally not planned for transit traffic. This has given a different priority to municipal policies. Other possible solutions is to install tolls for transit traffic or otherwise price mobility differently, or to change the overall traffic patterns.

It is just the start of the change of our urban landscape and mobility brought by real-time navigation apps. Expect physical changes and changes of usage.

Share

Why We Need to Work on Unfashionable Problems

I am getting fed up by the hype around fashionable ‘Artificial Intelligence’. Everything should be Artificially Intelligent nowadays (ref my previous post ‘How Automation Should Not Be Marketed as Intelligent‘). Thus I very much like this post of Paul Graham on ‘Fashionable Problems‘. His point is that too many people work on the latest fashionable technology or problem, and too little on other important aspects.

“Even though lots of people have worked hard in the field, only a small fraction of the space of possibilities has been explored, because they’ve all worked on similar things. Even the smartest, most imaginative people are surprisingly conservative when deciding what to work on. People who would never dream of being fashionable in any other way get sucked into working on fashionable problems.”

On this other hand this consideration also shows that there are great opportunities in working on other things than the latest fashion (although of course it may be much more difficult to get funded). And this is what I like to consider: non-conventional people that follow their interest irrespective of the latest fashion. Paul Graham reminds us actually that “The best protection against getting drawn into working on the same things as everyone else may be to genuinely love what you’re doing. Then you’ll continue to work on it even if you make the same mistake as other people and think that it’s too marginal to matter.”

Thus, do not worry too much about the latest fashion on tech. There are so many other areas where progress would be profitable for humankind. Don’t let yourself be deterred. Find what you’re passionate about and go for it!

Share

How the Paperclip-Maximiser Syndrome Has Become a Meme of AI

Have you heard about the paperclip-maximiser syndrome? It is a viral game and is used as a meme for negative consequences of a too powerful Artificial Intelligence. If this AI’s only objective is to improve paperclip production it may finally exploit all of Earth’s resources and beyond doing just that – destroying everything else in its path. This Wired Column explains the idea: ‘The Way the World Ends: Not with a Bang But a Paperclip‘. (an alternative AI meme seems to be the strawberry-picking AI transforming the Earth in a single strawberry plantation)

In this interesting speech ‘Dude, you broke the future!‘, Charlie Stross a known Science Fiction author refers to the Elon Musk feared singularity exactly as the “paper syndrome”… and then points wisely that “Musk isn’t paying enough attention. Consider his own companies. Tesla is a battery maximizer—an electric car is a battery with wheels and seats. SpaceX is an orbital payload maximizer, driving down the cost of space launches in order to encourage more sales for the service it provides. Solar City is a photovoltaic panel maximizer. And so on. All three of Musk’s very own slow AIs are based on an architecture that is designed to maximize return on shareholder investment, even if by doing so they cook the planet the shareholders have to live on. (But if you’re Elon Musk, that’s okay: you plan to retire on Mars.)” So it seems that Elon Musk is exactly doing what he fears AI would do.

This all serves to remind us that any “intelligence” should not pursue a single goal but a balanced set of goals, because maximizing a single indicator is always at the detriment of the overall balance. This is true in management, and could possibly take unexpected proportions when AI gets involved.

Share

How the Covid-19 is a ‘Common Cause Failure’ Crisis

An aspect which strikes me in the Covid-19 crisis is that it is a typical case of a situation made significantly worse by ‘common-cause’ failures. This is a typical situation in snowballing industrial accidents, which we now see unfolding at global scale.

‘Common-cause failures’ is a situation where the same root cause affects several aspects of the system, and specifically those aspects which were supposed to back-up each other. When they happen, they worsen significantly the outcome of incidents because they remove redundancy. Recent examples in large accidents include: tsunami and subsequent flooding in Fukushima, which damaged all redundant nuclear reactor cooling systems; a flock of geese that stuck both engines at the same time on the aircraft that finally landed on the Hudson river, etc.

For industrial risk engineers, preventing common cause failures is the number one action to prevent major accidents because major accidents by definition are accidents that will bypass all redundancies built in the system.

And this is exactly what happens with the Covid-19. A lot of complaints on the availability of hospital beds and medical supplies are based on the fact that no planning considered the simultaneous problem to happen nationwide and globally. Spare capacity elsewhere was not available any more to compensate for a local overwhelming need. Europe plans was relying on China providing supplies; US emergency response was relying on relocating local casualties to other states…

Thus Covid-19 is a common cause failure and this explains the extent of the snowballing crisis we observe, as many redundancies built in our institutions and supply chains have been affected.

Share

How to Address the Challenge of Weak Signal Detection

As mentioned in our previous post ‘How to Explain Covid-19 Blindness‘, the Covid-19 situation illustrates the more general challenge in complex systems to identify weak signals early and specifically, those that can, with some probability, develop into a crisis of significant consequences.

It is a challenge many organisations are regularly facing. For example in my professional field, project management in complex projects, the challenge to detect weak signals early and act on them is addressed by advanced project control approaches.

Nevertheless, it remains a difficult issue. This monitoring is prone to generate many false alarms; and some actions taken early will also avoid some of those weak signals develop into a situation or a crisis. Therefore, there is a risk that responsible bodies become fed up by too many weak signals and lose their vigilance. Still, maintaining this detection capability remains obviously essential.

In the Covid-19 situation as in some other challenges of humanity, the weak signal was identified and clearly delineated at least in some pockets of medical specialists, and even in some strategic analysis by the military. What was not anticipated was the consequential impact on the economy. This was probably because of a lack of pluri-discipline linkage and scenario planning. In addition there has been a lack of anticipation as soon as the first signs of a possible looming scenario appeared.

As a learning point, it is probably worth as in all complex system issues to setup a multi-disciplinary weak signal challenge team to review on a regular basis those signals and recommend actions.

Share