The rise and rise of Bitcoin


The price of bitcoin has gone up by over 16 times this year.

At the start of the year, one bitcoin traded at under $1,000. Now it is over $16,000.

What’s going on – and can it continue?

The price of bitcoin is driven, as are most things, by supply and demand.

Supply, in the case of bitcoin, is controlled by an algorithm, and new bitcoins are created by a process called mining.

In theory, the total number of bitcoins will not exceed 21 million by 2040.

According to Bloomberg, around 1,000 people control 40% of the market for bitcoin.

The currency’s elusive founder, Satoshi Nakamoto, is believed to own around 5% of the market, or 1 million bitcoins, worth around $16 billion at current prices.

The price has been driven up by the rest of us, competing to be part of the rise in valuation that has happened over the last year.

A relatively fixed supply and voracious demand are behind the increase.

Bitcoin has no government backing it and has no intrinsic value.

Its valuation is supported purely by the belief its community of users have in it.

That is no different, really, from any other currency though – we have to believe that the country backing it will still be there in the future.

An MIT Technology Review suggests that bitcoin may be at the point where it is as powerful as a government – China’s attempts to ban it have not stopped it.

The main problem at the moment with bitcoin is its volatility and a marketplace for bitcoin futures may help to stabilize its value.

The point is that while the supply of bitcoins is fixed, the supply of crypto-currencies is unlimited – anyone can set up a new one.

And the thing that is drawing people in is not that they really want to hold bitcoins – it’s that they really want to be able to convert the rise in their bitcoins to conventional currencies like the US dollar.

That’s speculation and could be hazardous to your wallet. Or make you very wealthy.

No one really knows.

How can you use analytics to create value?


It’s not clear for many of us exactly how we can use the data we have to help our organisations.

The systems we have are collecting more data than ever before – from customers, operations, sensors – and there is data being collected around us in social media platforms.

Many organisations are good at looking back – they can tell what happened and to some extent why it happened.

Fewer can see what is happening right now and what could happen next and what they should do as a result.

Some organisations use real time data to increase sales

The recommendation engines used by Amazon are designed to increase sales by showing you what else you might like – and the way in which google or Netflix present related information is designed to keep you using their platform.

But, what should the rest of us do to get started?

The advice from an MIT study is a perhaps a good point to begin.

There is more data around than we probably have the resources to deal with, so the starting point is to go after the big problems – the 20% of things that have 80% of the impact or cause the majority of the problems.

It’s hard to get people to give up personal decision making and rely on data.

Linking the analytics work to big, important things in the business and showing how data can help with those decisions has a better change of getting new methods into general use.

Then, it makes sense to start by asking questions instead of getting lost in the detail of analysis.

We can spend so much time getting data and starting to cut and analyse it that, all too often, there is no time left to see where it can help.

If we start with the big things that the company is interested in – often set out in their goals and objectives, we can then ask questions about what information would be useful to reach those goals.

Those questions will then let us explore what we can do to get answers and start to define the kinds of data and analyses we need to carry out.

The things organisations say are most important to them right now are trends, forecasts and standard reports.

The things that are likely to also become important are dashboards, simulation and scenario analyses, business process analytics and advanced statistical techniques.

We should try and make sure that what comes out of the analysis we do is business friendly and can be layered with other pieces of information and intelligence to make decision making easier.

Next – we build on what has been done before.

All too often, vendors are dismissive of spreadsheets.

In any room, however, it is likely that the vast majority have used and are comfortable with spreadsheets while advanced analytics tools have a steep new learning curve.

Good information systems design will keep what works and build more – perhaps with advanced analysis automated as much as possible or centralised within a support unit with experts that can help.

Finally – we should have some kind of a plan when building our information systems.

An information agenda, signed off by management, is a good way to oversee the process of sharing and using data better.

The study says that top performing organisations use analytics five times more than low performers.

For the rest of us, we should begin by understanding how to get started.

How can our information systems help us be more productive?


Productivity is defined as output per hour by workers – and it has flatlined in the UK over the last ten years.

Our output depends on the tools we work with – and information systems are a vital part of that toolkit.

There are some who argue that we have not invested enough in technology while others think that recent technological changes have less potential to transform productivity.

So – how can we select and implement information systems that will improve productivity?

William Delone and Ephraim McLean came up with a model to measure the success of information systems in 1993, which they they revised in 2003, called the D&M IS Success Model.

It remains one of the most influential theories in the field, cited in in thousands of papers, and is a useful one to keep in mind when looking at a new system.

The model has 6 dimensions that are linked together with process flows and feedback loops. They influence each other, and in turn some elements are influenced by others.

The model begins by looking at quality – and sees quality as having three dimensions: information quality, system quality and service quality.

Information quality is all about what the system stores, delivers to the user, and produces for the user.

System quality relates to how the system works – does it do what is needed quickly or not?

Service quality depends on the organisation behind the system – are they helpful and is there guidance that is easy to follow or not?

These dimensions need to be looked at independently to assess quality fully.

The next two dimensions are user based.

First there is the user and the system – and this needs to be looked at from two angles.

How does the user plan or intend to work with the system?

How does the system actually use the system.

The quality of the system directly affects these user choices – we may intend to use a system, but if it is of low quality or the information is not good enough, we probably won’t.

We can measure user satisfaction based on their experience of using the system and their feelings about quality.

The last dimension looks to measure net system benefits.

These need to be some combination of saving money, saving time, increasing productivity and increasing sales.

The D&M IS Success Model seems deceptively simple – but it is a “parsimonious framework” that organizes many of the success metrics from research.

If a system scores well on these six dimensions then it should help us be more productive.

How much will BREXIT cost the UK?


Is there a ZOPA between the EU and the UK when it comes to a divorce settlement?

A ZOPA stands for Zone of Possible Agreement – also called a bargaining range.

In any negotiation we end up with two roles – a seller and a buyer.

The seller has a high price they want to get and the seller has a low price they would like to get.

The amount wanted by the EU has been talked about as being in the range of €100 billion.

Brexiteers in the UK, on the other hand, want to pay nothing.

A ZOPA exists if there is an overlap between the minimum a seller will take and the maximum a buyer will offer.

Refining the numbers, it appears that liabilities for the UK may be in the region of €80 billion but offsetting claims could reduce the total to €60 billion.

Europe may now want around €50 billion euros of £44 billion while the UK might be offering around £20 billion.

Both sides are tight-lipped about the actual amount.

If the buyer’s maximum price is less than the seller’s minimum – £20 billion vs £44 billion, for example, a negative ZOPA exists.

It may be possible to overcome that with other incentives – for example agreement on worker rights, acceptance of EU laws and so on – all of which come with political and legal complexities.

In the end, however, if the two parties don’t reach an amount acceptable to both, there will be no ZOPA and both will have to walk away.

The negotiations continue…

Why you should trust your gut instinct


We’ve all probably been in a situation where we can’t decide between two options and someone has said to just flip a coin and let it decide.

It turns out that this may be a very good approach indeed – but not quite in the way we think.

For a long time we assumed that people were rational creatures, governed by a conscious mind that made decisions based on logic.

Relatively recent research found that we weren’t as logical as we though we were – behavioural economics showed that we acted in ways that weren’t consistent with logic.

Behavioural economics, in turn, is criticised by some because what happens in the laboratory does not always show up in the real world.

For example, altruism shows up more often than we expect in experiments, as people share when they have no need to do so – but at the same time do they only share because they are being watched in an experiment?

As we became increasingly aware of the vast number of things the brain does without any conscious intervention – useful things like breathing and digestion – some researchers also realised that our body seemed to know things before our minds caught up.

For example, if people played a card game with two sets of decks, one of which was rigged, they eventually worked out that something was wrong with one of the decks.

When researchers monitored players using methods like measuring galvanic skin response – how much they sweated – the found that their bodies seemed to figure out the wrong deck well in advance of their conscious minds catching up.

Somehow their bodies were taking in a lot more information through their senses and using all that to figure out what was going on – and reacting to their findings emotionally – raising heart rates and sweating more.

The work of Antonio Damasio, described in David Eagleman’s book Incognito: The secret lives of the brain, forms the foundation of much of this research.

Damasio, a neuroscientist, found that patients that had suffered damage to their brains and were no longer to process emotion and the kind of signals their bodies were giving them – so called somatic markers – were unable to make good choices.

In the card deck example above, they were unable to distinguish the bad decks, even after they had been told there was one.

In other words, our ability to make wise choices is fundamentally linked to the reaction our bodies have when confronted with the sensory data in front of us – and a completely rational Spock like approach will more than likely fail us.

What this means is that when we have to make a tough decision and flip a coin – instead of focusing on which side lands, we should be monitoring our body’s reaction.

If we have a sense of relief when a particular side lands or we get knotted up with tension – that is our body’s way of telling us which decision is the right one – through gut instinct.

Then, we should ignore the coin toss and go with our gut.

How to avoid being trapped in an echo chamber


The giant corporations that we interact with on the internet are trying to understand what we like so that they can feed us a processed diet of stuff that is similar.

Amazon, for example, suggests other books we might like. Newsfeeds on the iPhone change based on what we click on. Google creates personalised search results based on our search histories.

Mainstream news channels pick stories with an eye to what other news channels are likely to talk about – creating an increasingly homogeneous mix of information and news.

Many people simply choose to ignore stuff they don’t like or don’t agree with on the internet – creating a system where what they see is more of what they already see.

The main danger with this is that we could get trapped in an echo chamber.

Colin Raney has a nice way to show this in a 2×2 matrix – shown in the picture above.

When we see familiar content from familiar sources – that’s the time to be wary.

If people and sources we know are repeating the same thing it could be true.

Then again, it might simply be an echo, as the same thing bounces off and is repeated and amplified by others in our network.

We may end up with a distorted version of what is happening.

When we see familiar content from new, independent sources that might help confirm a story.

The idea of independence is key. The content needs to have been gathered and checked independently.

If we see new information from familiar sources, especially ones we trust, that might be something to explore further.

We may learn something in these circumstances.

Finally, when we see new content from new sources, we expand our horizons and are exposed to novel concepts, ideas and stories.

The technology and media that surrounds us are trying to understand us better and deliver more tailored content.

Paradoxically, in doing that, they might make it too easy for us to settle into a situation where we believe that what we see is all there is.

Politicians know this.

The strategy all over the world now is to talk only at people that already believe in them or that might be on the fence. The opposition can safely be ignored.

Comfort leads to complacency – instead we might want to be curious, look for facts, be sceptical and look at things from multiple angles.

Do we all need to think like journalists now?

Why science can’t help us to understand ourselves


We think we are much better off these days than people that went before us.

And, using virtually every measure that matters, that is the case.

As Steven Levitt and Stephen Dubner write in Superfreakononics, whether it comes to “warfare, crime, income, education, transportation, worker safety, health”, many of us live in the most hospitable of times.

The reason for this is the scientific method and its rational approach to reasoned thought.

Our use of the scientific method has helped us create the modern world through a system of thinking.

In essence, we look around us at everything and wonder why it is the way it is.

We look for explanations – create hypotheses – guesses at what might be going on.

Perhaps it rains when we do a dance, for example, or that the lines on our palms can tell what is going to happen to us.

Then, and this is the clever bit, we do something else.

We design an experiment to see if our hypothesis is right.

We collect real world data and see if what we predict will happen according to our hypothesis matches what happens in reality.

It seemed obvious for many years that a heavy object would fall faster than a light object when dropped from a height.

It took an experiment – dropping a heavy and light ball from a height and seeing when they hit the ground – to show that they fell at the same speed. Their weight made no difference to the outcome.

The experiment showed the hypothesis was false and so we needed another one.

The problem is that life quickly becomes more complicated from that point.

While the scientific method changed the course of human history and gave us cars, trains, planes, rockets and the rest of the modern world, it also created an illusion that it could explain everything.

And that’s wrong, it seems. The history of science shows that new hypotheses come along with irritating consistency to upend everything we know.

Simple Newtonian physics morphed into relativity and the simply incomprehensible world of quantum mechanics.

The logical end result of all this, as described in Pirsig’s Zen and the art of motorcycle maintenance, is that “The number of hypotheses that can explain any given phenomenon is infinite”.

What this means, in short, is that we think science will help us move towards the truth.

Where it matters, however, science simply increases the number of truths – what we believe to be true might not be true a few years from now.

Truth, rather than being fixed and unchanging, may be something we accept as true for the moment.

The point is that the scientific method has taken taken care of the material needs of many people – food, shelter, clothing.

The mistake we make is when we assume that because it explains so much, it explains everything.

The problem, again from Pirsig, is that the structure of reason based on rationality is “emotionally hollow, aesthetically meaningless and spiritually empty”.

We need to look elsewhere for meaning.

How to design a pilot


When we assume, Oscar Wilde wrote, we make an ass out of u and me.

Much of the time we’re not sure what option to take.

Whether its something simple, like changing the order in which we do things in the morning or a more complex situation, like deciding to move to a different country or go back to University for a graduate degree, we still don’t know how things will turn out.

With organisations – we face the same problems when trying to get a new customer to work with us, pick a supplier or decide in which projects we should invest company money.

So, how do we make a decision in these circumstances?

Some people decide, on the basis of their experience, that a particular course of action is appropriate. Then, they take things personally.

Questioning that approach is the same as questioning their competence or experience – which makes it difficult to have a discussion about the range of options.

This leads us down a binary decision path – either we do what is suggested or we don’t, and we might succeed or we might not.

The thing is that often the options are not really binary – there are more things we could do, if we were open to them.

Take the moving to a different country choice, for example. It’s different having a holiday in a country to moving there permanently.

It might be wise to try a longer holiday, see if there is a way to experience it for a three-month period, or spend some time asking people that have already done a similar move about their experiences.

These are pilots – experiments and research that try and test and validate our assumptions.

Ideally, pilots should be something quick, easy and cheap that we can try out and see whether an idea is worth doing and investing more time, effort and money in.

This happens all the time with new television programmes – a pilot episode is shot to test with audiences – and how they react may make the difference between getting funding to create a series or having to go back and start again.

Good pilots are designed, however, with one clear thing in mind.

They limit the downside, while leaving the upside uncapped.

This is what Nassim Taleb calls optionality in his book Antifragile.

Adherence to this principle, according to Monish Pabrai in The Dhandho Investor, is what has made a particular Indian community, the Gujaratis, so successful in the United States – owning tens of billions of dollars worth of assets.

Dhandho is a word that means “endeavours that create business” and it is based around a low risk-high return strategy that is all about “Heads I win; tails I don’t lose much”.

Taking a more well-known example – it’s what Richard Branson did when he set up his airline in 1984. He leased a single used Boeing 747-200 on condition he could hand it back after a year.

His downside was limited to a year of operating the plane and he knew that he could get money back for it if things went south.

Virgin Atlantic now operates 39 planes and turns over more than £2.5 billion a year with nearly 9,000 employees.

The opposite of an assumption, perhaps, is not a certainty but a pilot.

How are you?


The answer we give to a question like “How are you?” or “How’s it going” depends on how we’re feeling at the time.

But – why do we feel the way we do at a given time?

The field of personality psychology tries to explain that.

It appears that there are three levels at which personality can be explored – corresponding to Dan McAdam’s three level model of personality.

At the foundation of our personality are traits.

Our general tendencies, or disposition, is based on traits that can help describe our personality.

For example, the Big Five personality traits are Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism, remembered with the mnemonic OCEAN.

These traits describe characteristics such as being open to new experiences, how organized and dependable we might be, whether we are energetic in the presence of others, whether we are compassionate and trusting rather than suspicious and how easily we experience unpleasant emotions such as anger.

While traits might describe what we are – they don’t explain why we are what we are – because we are more than just traits.

One level up is what we do with our lives – the things we do day to day – something called a Personal Action Construct (PAC).

The PAC is a model we have in our heads about the facts of our life and the order in which we put them.

A specific PAC developed by the psychologist Brian Little is the Personal Project. Brian explains his ideas in his book Me, Myself and Us and an entertaining TED talk.

In his approach, Personal Projects comprise things like “personal strivings, life tasks, personal goals, current concerns and possible selves”.

Brian argues that Personal Projects can explain a lot about why people act the way they do. If we can understand someone else’s projects that can help us understand and work with them better.

At the same time, if the projects we have are ones that are tiring and stressful or not aligned with our traits and sense of purpose, it’s not surprising that we aren’t that happy.

At the top of the hierarchy are stories – what we tell others to make sense of our own lives so far and what we want to do in the future.

Stories are important – without a coherent narrative it’s hard for us or anyone else to understand our position.

Whether we’re looking for investment for a new company, trying to change jobs, or create a new product or service or figure out what to about responsibilities when getting older – the stories frame and tell will steer the actions we take.

The point about this 3 level model is that all of this happens without most of us being remotely aware of it – who thinks about their personality in terms traits, projects and stories?

The level to focus on, however, might be the second one – Personal Projects.

Traits are inbuilt. Stories are after the event. The projects are the here and now.

As John Ruskin wrote, “Tell me what you like and I’ll tell you what you are”

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