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Science Matters

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Here is the text of several Science Matters talks. They were presented as integrated PowerPoint and spoken presentations, so the documents below are just a representation of what went on at the actual talk.   They were interactive with the slides and the audience.

The Technologies that will make Renewable Energy Reliable:    February 2019  Allan Wilson


1. Climate Change: The case is made that climate change is serious.  Charts of local temperature show a clear rising trend.  This trend will continue – both because we are still adding to atmospheric CO2 at a fast rate and also because of the lag – the sea takes time to warm and ice takes time to melt so another degree of warming is already built in.  The nations of the world need to stop burning fossil fuels and start generating our power from renewables.  Energy from Nuclear Fission is also an option, but is not discussed here.


2. Intermittency: Slides showing the problem with solar and wind:  intermittency.  Examples from a house solar panel and from the national energy market (see https://opennem.org.au/#/all-regions).  You can see your own power useage in half hour intervals at the Powercor website https://www.powercor.com.au/customers/myenergy if you register with your own meter number.


 Many different ways that this problem may be tackled


3. CCS: Continue burning coal and go to Carbon Capture and Storage:  Being trialled at the Otway project.  It works but it is costly ($20-$70/ton CO2 stored underground in old oil wells) and uses up energy to do it.  It seems unlikely to be the solution because of cost.


4.  Demand Management: A partial solution is to reduce our demand.   This can come from better house insulation, more efficient appliances (particularly air cons) and ways of trimming peak demand (smart meters, paying big users to turn off on hot days, etc)  Smart controllers will assist – those that turn appliances on automatically when demand is lower. None of these changes really change the source of the power.


5.  Gas Turbines.   These can be started up in a short time (150 sec) and be used to fill gaps in supply when wind and sun stop producing electricity.  They are more efficient in producing power (per unit of carbon burnt) than coal and are regarded as a step towards a renewable target. They presently do not get paid to be on standby.  Economics depends on the legal framework under which power is paid for.


6. Lithium Batteries: The large Tesla batter built at Jamestown SA can supply power in 20 milliseconds, so it is better for stabilizing the grid than gas turbines (all power is not equal). Not the answer to long term storage.  Home batteries have caught the imagination of the populous and are being subsidized by governments.  However, their economics are poor.    Some think of using them to go off grid, but this is even worse.  It is hugely expensive to install enough batteries to fill in the long gaps in solar and logically silly  - why not stay connected so you can be paid for your excess power?


7. Pumped Hydro: Snowy 2 is planned to use cheap off peak power to pump water uphill, which is then used to generate expensive peak power when it runs back downhill again.  It doesn’t make any power – it shifts the power from one time to the next with an efficiency of 70-80%, so uses power to gain dollars and give reliability.  A similar pumped hydro scheme is planned for Spencer Gulf. It will use sea water pumped up to a high plateau.


8. Compressed Air:  A parallel idea stores the energy as compressed air.  A scheme planned for Strathalbyn in SA will use an old zinc mine as a storage chamber.  A column of water (pumped out of the mine) stabilises the pressure and heat exchangers are used to make the process more efficient.


9. Hydrogen:  Use surplus power to make hydrogen out of water.  Water is just H20 or hydrogen oxide, but it takes a lot of energy to split it into its two elements.  The hydrogen can then be stored either as compressed gas or as companion compounds methane and ammonia.  This is then used to power cars and buses.   The most efficient use is not to burn it in a conventional engine but to convert it directly into electrical energy by way of a fuel cell. A fuel cell splits hydrogen into protons and electrons and uses the electrons as electricity to drive the wheels.  A Toyota hydrogen fuel cell car, the Mirai, is expected to be on sale in Australia later this year.  However, there are as yet no filling stations. Hydrogen fuel cell buses will be a feature of the 2020 Olympics in Tokyo.  Lorraine Noonan noted that the ammonia could be used as Nitrogen fertiliser, thus replacing urea that is now made using natural gas.


There is a hydrogen plant planned for Adelaide.  Last year Turnbull made and announcement of a plan for a hydrogen plant in the Latrobe Valley using Brown Coal as a power source, but to be carbon neutral this needs CCS added on.  Labor recently made a statement that Gladstone Qld would become the hydrogen capital of Australia, but supplied no details.  However, the low round-trip efficiency of hydrogen leads some to doubt its future.


10. Grids:  The gaps in wind and solar power generation can, to some extent, be filled in by grids that transfer power from one place to another.  The eastern states form a grid, with the Bass Strait cable used to give value to Tasmanian hydro.  WA has the best balance of wind power with Eastern States, but is too far away for economic connection. Households should not think of going off grid.  We all need connections to fill in the gaps and obtain value from surpluses.


11. Transport:  We can run cars on electricity.  Their batteries might be charged off-peak, or peak. They could even be a part of the smart storage grid, when left in the garage.  How they will affect the timing of power demand in future is unknown.  Aircraft are much harder to manage with renewable energy.  Batteries are too heavy.  That leaves biofuels, but that requires farmland.  There seems to be no solution.


12.  Industry:  Some companies are working on plans to make steel from Hydrogen and other energy sources, rather than from coal.   These are not well advanced.


13. Future Dreams:  The Chinese have announced plans for solar power generation in space, where the suns shines continuously.  The idea is to beam that power back to earth by microwave or laser.  There are huge logistic and technological problems to be solved, so I call it ‘Pie in the Sky’.  


Power from the fusion of hydrogen into helium requires extreme temperatures that cannot yet be contained. . This is the process that generates the sun’s heat.  No one has yet demonstrated that it can be done on earth, but research continues.


14. Conclusion: 

“People always think about energy in the very simple mindset that actually only applies to coal. Coal is simple - you have one big coal power station connected to the city and it supplies all demand”


“The future energy system will be far more complex and messy. Many energy sources from many small power stations, interconnected, spread over a wide geographical area, with storage and demand management”    William Wilson (2019)



Thinking Fast and Slow Part 2


Presented March 19 2018    Allan Wilson


About decision making – this time with money – about decisions made under risk


Based on the Book Thinking Fast and Slow: Daniel Kahneman

Reminder of System One and System Two thinking:

About how the mind seems to work as 2 systems ;  System 1 and System 2


System One:   Quick and intuitive   - and more likely to be wrong.


System Two:  Slow and deliberative -  always there, but it is lazy, so often not activated when it should be.


Shown Picture of Angry women    Instant response – no effort - automatic – cannot stop it     System one. And there is no memory of other options rejected. [Remember the Bat and Ball question.  A bat and ball together costs $1.10.  The bat costs $1 more than the ball.  How much does the ball cost?  No it’s not 10 cents!]


 Contrast  image of "17 x 24 ="   Can work it out, but Slow and deliberative – orderly and effort-ful.  System two thinking.


 We think that we act like System two – making rational choices

But in fact we are more likely to use system one – the jump to conclusion mode


System 1                                          System 2

Fast                                                    Slow

Unconscious Reasoning                Conscious Reasoning

Implicit                                               Explicit

Automatic                                        Controlled

Low Effort                                        High Effort

Large Capacity                                  Small Capacity

Associative                                       Rule-Based

Evolutionarily Old                          Evolutionarily Recent

Nonverbal                                        Linked to language

Independent of working memory         Limited by working memory                                                                                capacity

Non-Logical                                       Logical




Today’s talk is taken from the fourth section of the book, labelled CHOICES


The research on this topic has been done with the same type of small puzzle questions: thus

Would you prefer?

  • The toss of a coin, with a chance to win $100 (heads) or a chance to win nothing (tails)


Much of this is about the psychology of money - for example:

  • Today Jack and Jill each have 1 million in the bank

  • Yesterday Jack had 2 million and Jill had $100,000

  • Are they equally happy today?Jack and Jill have different reference points


This flies in the face of traditional economic theory – which is that we all make similar rational choices about money.  Economists make all their predictions on how we will behave to a change in economic circumstances in a rational (economic maximizing!) manner.  But real Humans don’t behave in such a rational manner.  (Example Governments Home Insulation scheme).  Kahneman calls these two races of people;   Humans (real people) and the Econs (the theoretical rational people used by economists).  In economic theory, Jack and Jill have equal wealth, so they should be equally happy.


Reframe the situation into one of choices between gains and losses

Problem 1: Which do you choose?  

            Win $900 for sure OR take a 90% chance to win $1000

Problem 2: Which do you choose?

Lose $900 for sure OR take a 90% chance to lose $1000


Most people are risk averse in the first question and risk taking in the second.  The sure loss in the second is very aversive and drives the risk to avoid it.  Gains and losses are not equal. People take risk seeking when all options are bad.


Why does this matter?  This is not just a game – it flows into real life, such as decisions to buy Insurance, our attitude to Bonuses, the outcomes of Court cases and Generals deciding on whether to go into battle.


Many of the choices we face in life are “mixed”.  There is a risk of a loss and an opportunity for a gain and we must decide whether to accept or reject the gamble.  Thus   You are offered a gamble on the toss of a coin. If the coin shows tails, you lose $100. If the coins shows heads, you win $150.  Will you accept the gamble?


Most people say no.  So how high must the prospective gain be to balance the prospective loss?   A common answer is about $200.   That is about 2:1. But each person will be different.  Some people are more loss averse than others.


Also, the ratio moves as the stakes rise – people become more loss averse when the stakes are high.  There are some risks that you will not take, even if the return is millions.


Try another version

John’s current wealth is $200,000

Betty’s current wealth is $1,000,000


They are both offered the same choice between a gamble and a sure thing

            A gamble (50:50) to end up owning either $200,000 or $1,000,000

            Or the sure thing to end up owning $500,000. 


You can see that John and Betty will make different choices.  The sure thing makes John happy and Betty miserable.


This is rather similar to the “framing” idea that we talked about in the last talk.  Here we call it the “endowment effect”


Another choice: Ben has a base level job, with little pay and little time off – [like a part time job at a chicken farm].  He is offered a better job.  He can have either a raise of $10,000 or an increase in time off of 30 days a year.  He is indifferent which he prefers. The money is useful, but so is the time off.  His point of reference is poor on both scores, on the toss of a coin so he takes the pay.


Next year the firm is in financial difficulty and asks him to drop the pay and take the time off.  He resists.  He is experiencing loss aversion from his new point of reference. 


[In real life, people will resist selling their house for less than they paid for it, even if the whole market has gone down] Consider the position of apartment dwellers who own their own identical apartments in a block, except that they were bought at different times and prices.  There is a downturn in the market and a developer wants to buy the whole block and re-develop He offers to buy them all at a common price. Those that bought at the higher price are now less willing to accept a common price than those who bought at the lower price, particularly if they appear to have lost money.


Loss aversion

The observation of loss aversion spills over into many aspects of everyday life.

Take the study of golf players.  They play against a reference point: the par score for each hole.  Whilst ever stroke counts in terms of the final score – and hence the winner, some strokes seem to count more than others at a psychological level.


One study analysed 2.5 million putts, comparing those that will result in a par, versus those that will result in a birdie.  In this case, failure to score par (the point of reference) results in a loss, whilst failure to score a birdie results in a gain foregone, not a loss. The study found that the difference in the rate of success when going for a par (to avoid a bogie) was 3.6% better than when going for a birdie (one under).  Trivial you may think, but not so.  The difference amounts to one stroke per tournament – and this would have been worth an extra $1 million per season for Tiger Woods in his hey-day.  


This has important implications for negotiations.  Consider a court case where you stand to gain and I stand to lose.  Loss aversion creates an asymmetry that makes agreements difficult to reach. The concessions I make are my losses and your gains.  Inevitably I will place a greater value on them than you do.


The same principle applies to proposed reforms.  Plans for a reform designed to improve overall performance will produce many winners and some losers.  However the potential losers will be more active and determined than the potential winners and the outcome will be based in their favour.  [Example – the current piecemeal efforts at Tax reform in Australia]


Perceptions of fairness and attitudes to punishment.


A small business has one employee who is paid $20 an hour.  (Say a car repair shop).  There is a downturn in trade and another like business opens, and is able to attract staff when paying only $15 an hour.  It is taking custom away from the first business, so it reduces wages to $15 an hour.   Is this fair?     83% did not agree that it was fair.


Now consider this situation.  The current employee leaves and the business decides to pay a replacement $15 and hour.   Is this fair?   73% agreed. 


The entitlement is thus personal.  But there is no entitlement attached to the new employee.  


Concepts of fairness are reference dependent. And imposing losses on people can be risky if the victims are a position to retaliate.  (eg a Facebook campaign by a loser to boycott a business)  This can be a real issue. 

  • A change in tariff structure for water

  • Taxation reform where it is inevitable that there will both winners and losers.



The fourfold pattern of gains and losses


Pure economic theory, which economists use all the time, assume that people make rational decisions on money.   But psychology research shows that this is not the case.


Gambling chances are the basic stuff in this research.  Consider your attitude to changes of 5% chances for winning $1 million.


A.   From 0 to 5%

B.   From 5  to 10%

C.   From 60 to 65%

D.   From 95% to 100%


Cases A and D are much more impressive to our minds than cases B and C.

Case A transforms the situation.  We have gone from nothing to a possibility. 

Likewise, in case D we have gone from a probability to a certainty.  The other two cases change the mathematical probability by the same margins, but do not change the psychological value.


These are described as the “possibility” and the “certainty” effects within what is called “Prospect Theory”.    The possibility effect drives the mind to buy lottery tickets and to dream of winning the prize, despite the low probability.   The dream does not diminish despite the probability falling from say 1% to 0.00000001%, as in a lottery, which is a ridiculously low odd.


The certainty effect comes into play in other contexts, such as law suits.  You have inherited $1 million.  But your brother, who inherited nothing, contests the will.  Your case is going well and the lawyer’s estimates that you have a 95% chance of winning.  That chance is worth $950,000 on a pure probability basis.  Your brother, realising that the case is going poorly for him, offers to settle for $90,000, leaving you with only $910,000.  Will you take it?  This is the certainty factor. 


The decision weights given to various probabilities for gambles with modest stakes have been worked out as follows: Insert table


Here a probability of 1% is given a psychological value of 5.5% and a probability of 2% a psychological value of 8.1%.  At the other end of the scale, uncertainty comes into play and decreases the value of nearly sure bet.


But there is more in this than the various probability of gains.  What about the probability of losses?    Look at what happens in what is called the Four-Fold pattern.


Insert Table showing four fold pattern

The top line in each cell shows the odds or prospect.

The second line shows the emotion evoked by that prospect

The third line how most people behave when offered a choice between a gamble and a sure gain or loss.

The fourth line describes the expected attitude of defendant and a plaintiff as they discuss a settlement in a civil case.


The top left is where people are averse to risk when considering the prospects of a large gain.


The bottom left involves the possibility effect and explains why lotteries are popular.


The bottom right is where insurance is bought.  All three are expected from the previous insights above.


The top right, though, is unexpected. Here we see people as risk seeking when all the options are bad. Why?


First losses are very aversive.  Then there is the gamble, sort of like the reverse of the lottery, where the small odds are ignored. Then there is the decision weighting, where participants only give a 80% weighting to a 95% probability.    


Now return to our lawyers and their clients above.  The plaintiff is in the top left – with the attraction of a sure gain and the fear of intense disappointment and regret if he rejects the settlement and then loses.   He is likely to be risk averse and settle.


On the other hand, the defendant in the same case is in the top right.  There is the temptation to fight on – the settlement proposed is almost as painful as the full loss, and there is a glimmer of hope to still win – a gamble, so he will be risk seeking. The defendant will settle for less than the statistical probability would suggest.  Analyses of both actual and mock trials show that this is what happens in real life. Similarly, generals often fight on far past the point where the loss is certain and go on to annihilation.


Rare events

Kahneman’s research show that rare events (eg a terrorist attack) are both overestimated in probability (that it will happen) and overestimated in their weight (ie impact).   The outcome is that for a time after an attack, people will avoid the locality of the last attack, despite the probability of it being repeated there are extremely low (particularly as that locality will be swarming with police for weeks afterwards, so the probability, already low, will be even lower).  Hence tourists avoid Spain – or Paris – or wherever, for weeks after and attack. This is system one thinking, which you cannot avoid, even if you believe the stats.  


These are outcomes of intuitive thinking – which we called System 1 thinking – the consistent overweighting of improbable outcomes – which leads to inferior outcomes compared to System 2 thinking – from the mathematical odds.  People do this all the time – for instance think about the improbably low odds of risk from nuclear power – or the very low odds of a carcinogen affecting you. 


One aspect involved here is vividness and ease of imagining affect our choice.  Participants were given a choice between two urns containing white and red marbles.  Drawing a red marble led to a prize.  From which urn will you draw your marble?


Urn A contains 10 marbles, one of which is red

Urn B contains 100 marbles, eight of which are red


The probability of drawing a winning marble is lower in Urn B than in urn A, yet 30-40% of students chose Urn B, where 8 marbles provides a more vivid outcome than 1 marble.


In another study, people who saw information about a disease that kills 1,286 people out of 10,000 as more dangerous than a disease that kills 24.14% of the population, despite the first being only half as likely as the second.


The power of format opens the way for manipulation. A good lawyer who wishes to cast doubt on DNA evidence will tell the jury that the chance of a false match is 1 person every 1000.  The prosecutor will present the same information as a false match every 0.1%, which seems, to system 1, as much less.  The first is much more vivid, whilst the second is more mundane.  I saw this last week in the paper.  Bill Shorten said that only 10% of people with a pension or part-pension would be affected by his policy on changes to the tax status of dividends.  The other side said that this would be 250,000 people. 




Regret is a punishment that we inflict on ourselves.


Consider the following scenario:

Mr Brown almost never picks up hitch hikers.  Yesterday he did and was robbed.

Mr Smith often picks up hitch hikers.  Yesterday he did and was robbed.

Which of the two will experience greater regret over the episode?


88% of respondents said Mr Brown.  12% said Mr Smith. 


But regret is not the same as blame.  Other participants were asked: “Who will be criticized most severely by others?   The results were Mr Brown 23% and Mr Smith 77%.


Regret and blame are both evoked by comparison to a norm and the relevant norms are different. Decision makers know that they are prone to regret and the anticipation of that powerful emotion plays a part in many decisions.  You see this in the following example.


Paul owns shares in CBA.  During the past year he considered switching to BHP, but decided against it.  He now learns that he would be $1200 better off if he had switched.


George owns shares in BHP.  During the year he switched to CBA.  He now learns that he would be $1200 better off if he had not switched to CBA.


Who feels the greater regret? The results are clear cut.  8% said Paul and 92% said George. Yet the situation of the two investors is identical.   There is stronger emotional reaction to an outcome that arises from an action than from an inaction.


The asymmetry in the risk of regret favours conventional and risk-averse choices.  Consider a doctor who faces a treatment decision with a very sick patient.  He can risk a new unconventional treatment that has had some good successes overseas, or stick with the conventional treatment.  The doctor who prescribes an unusual treatment faces regret, blame and perhaps litigation.  The potential benefit from the success of the unusual treatment is smaller than the potential costs associated with its failure.  


Kahneman says that you can take precautions to avoid regret after important decisions by being explicit about the anticipation of regret.  If you anticipate the possibility of regret you will feel less of it. There will also be hindsight bias, which you can reduce by being very thorough in your decision making.



There is good reason to see believe that the administration of justice is often incoherent in several ways. Consider the outcome of experiments with mock juries. In this example, mock juries were first shown one of these cases separately.  Half were shown one case and half were shown the other.


Case 1.  A child suffered moderate burns when his pyjamas caught fire as he was too close to an open fire.  The company that made the pyjamas had not made them fire resistant.


Case 2.  The unscrupulous behaviour of a bank caused a customer to lose $10 million dollars.


They were then shown the two cases together. 


In the single evaluation the jurors awarded higher damages to the defrauded bank customer than to the burned child, perhaps because the financial loss provided a high anchor.  However when the two cases were considered together the jurors increased the award to the child to more than the award to the customer. A new comparison and anchor had been established which was reflected in the award to the child.


The first judgement in single cases is system 1 intuitive thinking. Wider comparisons involve system two thinking, which is more likely to produce more stable and rational outcomes.  However, juries, in assessing damages are now allowed to consider other cases (This is a USA book), which is an incoherent aspect of the legal system.


Another example is the difference between jurisdictions in possible penalties.   Under Occupational Health and Safety regulations the fine for a “serious violation” concerning worker safety is a maximum of $7,000.  A violation of the Wild Bird Conservation Act can result in a fine of up to $25,000.  Each penalty might seem Okay when seen in isolation, but seem grossly unfair when seen in comparison.


I see the same strange situation in Australia where the penalties for cruelty to wildlife seem to be much higher than penalties for not cruelty (violence) to people.