No, I’m no super lady, I don’t have no game whatsoever,
I put my high heels on and see how that goes, yeah
– Pauline, Sucker for love
Ask a mathematician why they like maths, and they will tell you that mathematics gives a definite yes or no. There is beauty in clarity. And, everyone likes to feel that they understand and have control over what is happening in their world. This feeling of certainty is reflected in the bottom two rows of Maslow’s hierarchy of needs: physiological and safety needs.
Tapping into fear and belonging
That said, we also love variety and surprise, which is the most popular information shared on social media. We crave new stimulus which is why we love games. We love the idea of chance or fortune transforming our lives for the better, and surely if we learn the rules, then we will succeed. And, that is why marketing has such a pull on us. Marketers tell us that we will have improved lives if we do/buy/or have what they are selling, and, marketers themselves will have improved lives too if we do/buy/or have what they are selling.
Tapping into belonging is another way to market, which is why the connection economy and building friendship with your customers is gaining so much traction as a marketing strategy.
Modelling emotion and what-ifs
Modelling human emotion is impossible to do with game theory especially on social media, a fluid, still unknown, type of communication. We will never quite know who our audience is. We may target our demographic, but if they retweet or share something outside of that, then you never exactly know who is looking at your content, or how they will react to it. All game theory can do is offer interesting and potentially useful partial explanations to model a selection of what-ifs scenarios when employing different strategies.
In the last post (part 3), we looked at various game theory strategies from the aggressive to the altruistic, and saw that people generally behave like the people around them (hawk-dove) and that Kermit was in a bit of hurry to get together with his girl, which caused him to behave passive-aggressively, and probably not get what he wanted.
Don’t be like Kermit
Game theory is a tool for social media marketing and the best application of it is recording trial and error attempts (with statistical significance) whilst using our emotional intelligence.
Be aware of your emotions and triggers (your personal competence) so you don’t get involved in a big wrangle either privately, which could damage a relationship, or publicly, which might be retweeted everywhere and could wreck your brand or reputation. Even in the mathematics of game theory we need to understand other players moods and motives (social competence) and not assume anything. We need to ask for more clarification, so that when we do make a move, we do so with clarity and certainty that we are doing the right thing, and as any mathematician would tell you if you asked them, there is beauty in clarity for it gives us certainty and a sense of control, things which are harder to come by in our ever changing world.
Kermit drinking his tea and throwing shade makes me laugh. However, I think we all understand his frustration. It seems that in business and personal relationships, people play games. We may not know why, and we may not know the rules. But as we saw in part 2, before we react, we might want to find out more: if a game is being played, which one, and if we want to play or not.
Games, payoffs, and winning
A game is normally defined as having two or more players, who have a choice of possible strategies to play which determine the outcome of a game. Each outcome has a payoff which is calculated numerically to represent its value. Usually, a player will want to get the biggest payoff possible in order to be certain of winning.
Dominance, saddles, and mixed strategies
Playing the strategy with the biggest payoff is known as the Dominance Strategy, and a rational player would never do otherwise, but it’s not always easy to identify which strategy is best.
So, players sometimes take a cautious approach which will guarantee a favourable result (also known as the Saddle Point Principle). Other times, there is no saddle point so players have to choose at random what strategy to play and hope for the best. They can calculate the probability of mixing up strategies and their chances of winning. If their probability skills are not great they can play experimentally and record their results 30 times (for statistical significance) to see which strategies work.
How does this work on social media? Well, no one knows how social media works so a trial and error approach whilst recording results can be useful. Luckily, Twitter and Facebook both provide services and stats to help.
Free will, utility, and Pareto’s principle
A major question is whether players have free will or not and whether their choices are predetermined based on who they are playing with and the circumstances in which the game takes place. This can depend on the amount of information players have available to them, and as new information becomes available, they play a specific strategy, thus seeming as if they didn’t have free will at all.
Players assign numbers to describe the value of the outcomes (known in economics as utility theory) which they can use to guide themselves to the most valued outcome.
This is useful if we have a game where the winner doesn’t necessarily take all. If the players have interests which are not opposed and by cooperating the players can end up potentially with a win-win situation or at least a situation where everyone gains some benefits and the solution is not the worst outcome for everyone involved. This is known as the Pareto Principle.
On social media? Retweeting and sharing other’s businesses news is a nice way of ensuring everyone gains some benefits because with a potential market of 307 millions and there is enough of a market to go around for everyone to win-win and of course, reciprocate.
The Nash equilibrium
Taking this further is the Nash equilibrium which was named after John Nash, who proved that every two player game has one equalizing strategy (either pure or mixed) in each game. By looking at the equilibrium strategies of the other players, everyone plays to equalize. This is because, no player has anything to gain by changing only his or her own strategy, so it is win-win.
Are you chicken?
Ducks have been known share out the bread thrown to them so they all get some rather than one duck eating everything. This is known as the Hawk-Dove approach in game theory. When there is competition for a shared resource, players can choose either conciliation or conflict.
Research has shown that when a player is naturally a hawk (winner takes all) and plays amongst doves, then the player will adapt and cooperate. Conversely a dove amongst hawks will adapt too and turn into a fighter.
If there are two hawks playing each other the game is likely to go chicken, which is when both players will risk everything (known as mutually assured destruction in warfare) not to yield first.
We adapt very easily to what is going on around us, and on social media this is totally the same. In a 2014 study Pew Research Center found that people are less likely to share their honest opinions on social media, and will often only post opinions on Facebook with which they know their followers will agree – we like to conform.
The volunteer’s dilemma
In contrast, the volunteer’s dilemma is an altruistic approach where one person does the right thing for the benefit of everyone. For example, one meerkat will look out for predators, at the risk of getting eaten, whilst the rest of the meerkats look for food. And, we admire this too. We love a hero, a maverick, someone who is ready to stand up and be different.
The prisoner’s dilemma
But we hated to feel duped which is why the prisoner’s dilemma is one of the most popular game theories of all. Created by Albert W. Tucker in 1950, it is as follows:
Two prisoners are arrested for a joint crime and put in separate interrogation rooms. The district attorney sets out these rules:
If one of them confesses and the other doesn’t, the confessor will be rewarded, the other receive a heavy sentence.
If both confess each will get a light sentence. Which leads to the belief that:
If neither confesses both will go free.
It is in each prisoner’s interest to confess (dominant strategy = 1) and if they both do that satisfies the Pareto principle (2). However, if they both confess, they are worse off than if neither do (3).
The prisoner’s dilemma embodies the struggle between individual rationality and group rationality which Nigel Howard described as a metagame of a prisoner cooperating if and only if, they believe that the other prisoner will cooperate, if and only if, they believe that the first prisoner will cooperate. A mind boggling tit-for-tat. But, this is common on Twitter with those: Follow me, I will follow you back and constant following and unfollowing.
And, in any transaction we hate feeling like we have been had, that we were a chump, that we trusted when we shouldn’t have, which is why some people are so angry and like to retaliate. Anger feels better than feeling vulnerable does. But, great daring starts with vulnerability, the fear of failure, and even the failure to start, the hero’s quest shows us that.
Promises, threats, and coalitions
As we add more players, all rationality may go out of the window as players decide whether to form coalitions or to perform strategic style voting. If we introduce the idea of the players communicating then we add the issues of trust in promises, or fear of threats and it all starts to sound rather Hunger Games.
On social media aggression and threats are common, because of prejudice, or group think, especially on Twitter where there is no moderation. And, online and off, we have all been promised things and relationships which have ultimately left us disappointed, and told us that we have been misinformed, like the fake news, we’ve been hearing about a lot lately. Fake news is not new, in other contexts it is known as propaganda. And, if it is not completely fake, just exaggerated, well that’s not new either, New Labour loved spin which led to a sexed up dossier, war and death.
Kermit’s next move
Philip D. Straffin says in his book Game theory and strategy, that game theory only works up to a point, after which a player must ask for some clarification about what is going on because mathematics applied to human behaviour will only explain so much.
And so we turn back to Kermit. What is he to do? He has passive-aggressively asked for clarification and had a cup of tea. What’s his next move? Well, he could wait and see if he gets a reply (tit for tat). Who will crack first (chicken)? But, with the texts he has sent her, it is likely that her response is somewhat predetermined, or perhaps not, perhaps she will repond with Nash’s equilibria, or at the very least the Pareto principle of everyone not getting the worst outcome.
Alternatively, he could take a breath and remember that he is talking to someone he likes and with whom he wants to spend some time, someone human with the same vulnerabilities as him. He could adopt the volunteer’s dilemma approach and send her an honest text to explain that his feelings are hurt, he thought they had something special, and that she liked communicating with him as much as other people. By seeking clarification in this way, Kermit may just end up having a very nice evening after all – or not. Whoever said: All’s fair in love and war, didn’t have instant access to social media and all the complications it can cause.
The earliest proof we have, so far, dates back to 3600BCE: Six-faced dice with coloured pebbles made from heel bones of sheep and deer have been found on archaeological digs in Assyria, Sumeria, and Egypt.
By the time of the birth of Christ, many types of random number generators, including dice, were common, and were used for betting on or with board games. They were often spoken of as the workers of the blind goddess of fate, fortune, or destiny. And, it says in the Bible, that they cast lots to decide how to divide up Jesus’s possessions (Matthew 27:35). Even nowadays we talk about the roll of the dice when we talk chance and the things which happen to us.
By 10th century Europe, cards were the most popular thing with which to play games. There might be some skill, but really, a lot of it is up to chance, and don’t we all know that cliche about playing the hand you were dealt?
Highs and lows on the roll of a dice
The first formal attempt at analysing games, especially of chance, was written in 1520 (but published in 1663) by Gerolamo Cardano and has been recognised as the first step in probability theory. Cardano was a compulsive gambler, so would have felt the highs and lows of the roll of the dice more than most. He was foremost in the minds of Pascal and Fermat who published a book in 1654, continuing his work. And, it was Fermat’s last theorem which remained a phenomenon until it was solved in 1994. Imagine, it took three hundred and fifty years to solve a puzzle.
Later, writer Fyodor Dostoyevsky described our love of excitement and chance when playing games and how our fortunes can flip in an instant. He wrote about it in letters to his sister and his short novel, The Gambler. He was convinced that you needed to detach and keep a clear head, but had difficulty doing either, for it is much easier said than done. Consequently, gambling and games are ubiquitous, from church bingo to nationwide lotteries. Life can really change with a roll of the dice – or so it seems.
But, it has to be said, game theory isn’t the same as gamification, at all. Please don’t mix them up. Gamification is about turning things into games such as business objectives and anything else we want to make more engaging and more fun. When gamification is well designed, it works really well. But game theory is much bigger, and much more than just games.
In 1944, von Neumann and Oscar Morgenstern translated and expanded von Neumann’s theories in order to produce: The theory of games and economic behaviour. For his 1928 paper was mainly about two people playing a game together with only one winner (known as: two person game-zero sum) but game theory is much bigger than this, and it is not just about games and game playing.
It might be based in mathematics, but game theory has people in it, of course, which is why it can be used to think about everything: economics, political science and psychology. And, it has the crazy assumption that people behave rationally, which if there is one thing I know about life, people never behave rationally, nor should you expect them to. The other thing is that, we can only partially model any prescription because the world is huge and constantly changing, and we can never model everything in a computer. It really doesn’t matter how clever computers get. We have a long way to go yet when modelling humans and behaviour, but game theory is a start.
That said, power is the name of the game: group voting, economic theory and how to influence people, especially in areas like interpersonal cooperation, competition, conflict, labour negotiations, and economic duopolies, can all be understood in terms of game theory.
Game theory for explaining social media
Social media is the big new tool of the Internet, for business, politics, etc, and as of yet, no one knows how it works. So, this series is going to take a look at some of the big hitters of game theory: the prisoners dilemma, the Nash equilibrium, and so on, to see if these strategies can help us understand better how social media works. Are people cooperating or conflicting in ways these models describe on social media? If yes, can we understand and anticipate behaviour? If not, what other theories could we come up with?
As a computer scientist I have spent hours talking to designers, architects and engineers to capture their domain knowledge to model in a computer, with the end goal of helping them do their jobs better. It isn’t always straight forward to perform knowledge elicitation with people who have been doing complex tasks, very well, for a long time. Often, they can no longer articulate why or how they do things. They behave intuitively, or so it seems. So, I listen to them as they tell me their stories. Everyone has a story. Everyone! It is how we communicate. We tell stories to make sense of ourselves and the world around us.
Up until now, stories have been the most effective way of transferring information but once we involve a computer, we become very aware of how clever and complex we humans are. With semiotics, we study how humans construct meaning from stories; with semantics, we are looking at what the meaning actually is. That is to say, when we link words and phrases together, we are creating relationships between them. What do they stand for? What do they mean?
English Professor Marshall McLuhan who termed the phrase the medium is the message, described reading as rapid guessing. I see a lot of rapid guessing when my daughter reads aloud to me. Sometimes, she says sentences which are semantically correct and representative of what happens in the story, but they are not necessarily the sentences which are written down. She is basically giving me the gist. And, that is what our semantic memory does – it preserves the gist or the meaning of whatever it is we want to remember.
Understanding the gist, or constructing meaning, relies on the context of a given sentence, and causality – one thing leads to another – something humans, even young ones like my daughter, can infer easily. But this is incredibly difficult for a computer even a clever one steeped in artificial intelligence and linguistics. The classic example of ambiguity in a sentence is Fruit flies like a banana, which is quite funny until you extend this to a whole model such as our legal system, expressed as it is in natural language, and then it is easy to see how all types of misunderstandings are created, as our law courts, which debate loopholes and interpretations, demonstrate daily.
Added to the complexities of natural language, humans are reasoning in a constantly changing open world, in which new facts and rules are added all the time. The closed-world limited-memory capacity of the computer can’t really keep up. One of the reasons I moved out of the field of artificial intelligence and into human-computer interaction was because I was interested in opening up the computer to human input. The human is the expert not the computer. Ultimately, we don’t want our computers to behave like experts, we want them to behave like computers and calculate the things we cannot. We want to choose the outcome, and we want transparency to see how the computer arrived at that solution, so that we trust it to be correct. We want to be augmented by computers, not dictated to by them.
For example, when we go to the supermarket, we follow a script at the checkout with the checkout operator (or self-service machine):
a) the goods are scanned, b) the final price is calculated, c) we pay, d) our clubcard is scanned, and e) we might buy a carrier bag.
Unless we know the person on the cash desk, or we run into difficulties with the self-service checkout and need help in the form of human intervention, the script is unlikely to deviate from the a) to e) steps above.
This modelling approach recognises the cognitive processes needed to construct semantic models (or ontologies) to communicate, explain, and make predictions in a given situation which differs from a formal models which uses mathematical proofs. However, in these human centred situations a formal proof model can be inappropriate.
However, either approach was always done inside one computer until Tim Berners-Lee found a way of linking many computers together with the World Wide Web (WWW). Berners-Lee realised that having access to potentially endless amounts of information in a collaborative medium, a place where we all meet and read and write was much more empowering than us working alone each with a separate model.
And, then once online, it is interesting to have social models, like informal community tagging improves Flickr and del.icio.us. Popular tags get used and unpopular ones don’t, rather like evolution. In contrast formal models use proofs to make predictions so we lose human input and the interesting social dynamic.
Confabulation and conspiracy
But it is data we are interested in. Without enough data points in a data set on which we apply a model, we make links and jumps from point to point until we create a different story which might or might not be accurate. This is how a conspiracy theory gets started. And, then if we don’t have enough data at all, we speculate and may end up telling a lie as if it is a truth which is known as confabulation. Ultimately having lots of data and the correct links gives us knowledge and power and the WWW gives us that.
Freeing the data
Throughout history we often have confused the medium with the message. We have taken our most precious stories and built institutions to protect the containers – the scrolls and books – which hold stories whilst limiting who can access them, in order to preserve them for posterity.
Now, we have freed the data and it is potentially available to everyone. The WWW has changed publishing and journalism, and the music industry forever. We have never lived in a more exciting time.
At first we weren’t too bothered how we were sharing data, pictures, pdfs, because humans could understand them. But, since computers are much better at dealing with large data sets, it makes sense for them to interpret data and help us find everything we need. And so, the idea of the semantic web was born.
The term semantic web was suggested by Berners-Lee in 1999 to allow computers to interpret data and its relationships, and even create relationships between data on the WWW in a way in which only humans can do currently.
For example, if we are doing a search about a person, humans can easily make links between the data they find: Where the person lives, with whom, their job, their past work experience, ex-colleagues. A computer might have difficulty making the connections. However, by adding data descriptions and declaring relationships between the data to allow reasoning and inference capabilities, then the computer might be able to pull together all that data in a useful coherent manner for a human to read.
Originally the semantic web idea included software agents, like virtual personal assistants, which would help us with our searches, and link together to share data with other agents in order to perform functions for us such as organising our day, getting more milk in the fridge, and paying our taxes. But due to the limitations of intelligent agents, it just wasn’t as easy to do. So, the emphasis shifted from computers doing the work, to the semantic web becoming a dynamic system through which data flows, with human intervention, especially when the originator of the data could say: Here machine interpret this data this way by adding machine friendly markup.
Cooperation without coordination
It seems strange to contemplate now, but originally no one believed that people would voluntarily spend time putting data online, in the style of distributed authorship, but we have Wikipedia, DBPedia, GeoNames to name but a few places where data is trustworthy. And, we have W3C which recommends the best way to share online.
The BBC uses websites like the ones above and curates the information there to ensure the integrity of the data. That is to say, the BBC works with these sites, to fact check the data, rather than trying to collect the data by itself. So, it cooperates with other sites but does not coordinate the output. It just goes along and gets what it needs, and so the BBC now has a content management system which is potentially the whole of the WWW. This approach of cooperation without coordination is part of what has become known as linked data, and the WWW is becoming the Web of Data.
Linked Data and the Web of Data
Linked data is a set of techniques for the publication of data on the web using standard formats and interfaces so that we can gather any data we need in a single step on the fly and combine it to form new knowledge. This can be done online or behind enterprise firewalls on private networks, or both.
We can then link our data to other data that is relevant and related, whilst declaring meaningful relationships between otherwise arbitrary data elements (which as we have seen a computer couldn’t figure out by itself).
Google rich snippets and Facebook likes use the same approach of declaring relationships between data in order to share more effectively.
Trust: Data in the wild, dirty data, data mashups
It all sounds brilliant. However, it is impossible to figure out how to get your data mashup right from different sources when they all have different formats. This conundrum is known as data in the wild. For example, there is lots of raw data on www.gov.uk, which is not yet in the recommended format.
Then, there is the problem of dirty data. How can we trust the data we are getting if anyone can put it online? We can go to the sites we trust, but what if they are not collecting the data we need? What if we don’t trust data? What if we use the data anyway? What will happen? These are things we will find out.
How can we ensure that we are all using the same vocabularies? What if they are not? Again, we will find a way.
The main thing to do when putting up your data and developing models is to name things as meaningfully as you can. And, whilst thinking about reuse, design for yourself, do not include everything and the kitchen sink. Like all good design, if it is well designed for you, even if you leave specific instructions, someone will find a new way to extend and use your model, this is guaranteed. It is the no function in structure principle. Someone will always discover something new in anything you design.
So what’s next?
Up until now search engines have worked on matching words and phrases, not what terms actually mean. But, with our ability to link data together, already Google is using the knowledge graph to help uncover the next generation search engine. Facebook is building on its open graph protocol whilst harvesting and analysing its data to help advertisers find their target audience.
Potentially we have the whole world at our fingertips, we have freed the data, and we are sharing our stories. It may be written in Ecclesiastes that there is nothing new under the sun, but it is also written in the same place: Everything is meaningless. I think it is wrong on both counts, with this amount of data mashup and collaboration, I like to believe instead: Everything is new under the sun and nothing is meaningless. We live in the most interesting of times.
Humans are overwhelmed with information both online and offline, a desire to understand the world around us, and for it all to make sense. So, we look for patterns and signs, and stories to reduce complexity into something more manageable.
At the same time we love to be surprised and delighted with variety, which is shown by the information users focus on most on social media and by our love of twist in the tale stories and thrillers. And, we use stories most of all to find meaning in our own lives and in everything around us.
We are moving into a most exciting time with the Internet of Things and our Digital Culture which is all part of the Connection Economy. We are only one click away from each other and our devices are all communicating with each other constantly. And, in this world we feel that we must be somehow always connected. It is difficult to disconnect for even a little while, for disconnection is our greatest fear.
Throughout this series we have looked at the various ways designers can manage our expectations and give us cues to manage how we behave with the technology before us. We have even see how designers can manage their own information overload with types and schemas. But, it seems to me that as we advance further into this digital landscape, today, the designer’s job is to now to make sure that we harness the power of technology in the right way. In the past, society was formed by technological advance, and we were just carried along with it regardless of our opinions.
We need our designers to design for the good, to protect humans from even more overwhelm and to support us as we work, and in the same way that a good design solution can come from constraints and boundaries, we need these online. Feedback with care: Hey you’ve been online for hours now, go to bed, we will all still be here later.
Designers are change agents whose job is to make the world easier for us to live in offline and online. Let us all learn to design for that – an easier world for us to live in.