Six Male Personalities of Modern Men Ranked. Which one are you?

 

1. Alpha Males Alpha males are assertive and dominant. They often take charge in social settings, have high social status, and are charismatic leaders. They're the center of attention, like a CEO who can effortlessly command a room.

Examples of alpha males in movies include King Leonidas, Captain America, and Thomas Shelby. These characters are strong leaders, exhibit courage, and inspire those around them, often fighting for principles or causes bigger than themselves.

2. Beta Males Beta males are less dominant and more agreeable and cooperative. They play a passive role in social hierarchies and crave forming relationships. A supportive colleague who willingly takes on a secondary role is a typical beta male.

Characters like Ron Weasley, Samwise Gamgee, and Bruce Banner are beta males who excel in supportive roles.

3. Delta Males Delta males often go unnoticed in social settings. They are neither dominant nor submissive and are fairly average in social interactions. Their adaptability makes them reliable, but they rarely lead.

An individual who follows instructions well but isn't a natural leader represents a Delta male. Harry Potter, Marty McFly, and Frodo Baggins are Delta males who reluctantly take on leadership roles when needed.

4. Gamma Males Gamma males are contrarians, often rejecting or challenging social norms. Their independent thinking sometimes makes them outsiders. A rebellious artist who struggles socially but is intelligent in other ways encapsulates this type.

Tony Stark, Jack Sparrow, and Han Solo are gamma males who challenge social expectations. They are highly independent, intelligent, and a bit rebellious, making them stand out.

5. Omega Males Omega males are at the bottom of the social hierarchy and often lack social skills. They are typically introverted and may not lack self-confidence but are generally unpopular.

They are often focused on specific interests, like a nerd who prefers solitude in his hobbies. Peter Parker, Newt Scamander, and Walter Mitty are Omega males who may struggle with social interactions but are capable of extraordinary things when focused on their passions.

6. Sigma Males Sigma males reject traditional social hierarchies and value their independence and self-reliance. They share charismatic traits with Alphas but operate outside conventional norms. A lone wolf who doesn't seek validation from others embodies this archetype.

Bruce Wayne, James Bond, and John Wick are Sigma males who operate outside traditional hierarchies. They are charismatic, self-reliant, and work alone, unfettered by societal expectations.

In conclusion, an alpha is the leader, often naturally assuming authority. A beta serves as a helper, supporting the alpha and contributing to group cohesion. A Delta is average, blending into the background and fulfilling essential tasks without standing out.

An Omega is at the bottom, excelling in specialized skills but often overlooked. A sigma is a lone wolf, holding a top position outside the established social hierarchy. Finally, a gamma, known as the rebel, operates outside conventional norms, questioning authority.

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The dynamics of crowds

 

How will Dorset react to climate change? Photo: Saffron Blaze, CC BY-SA 3.0.

Chris Budd

This article is based on a talk in Chris Budd's ongoing Gresham College lecture series. You can see a video of the talk below and see other articles based on the talk here.

At a recent maths conference on the impacts of climate change on the human environment, I met a researcher whose current project was to "model Dorset". When I pressed them on what they meant by this, they said that they were trying to model how the tourist industry and associated local businesses in Dorset would be affected by possible changes in the weather and the climate.

This would involve trying to model how crowds of people would react to such events as an extreme weather situation. Naturally this proved to be a very challenging problem, but on the grounds that "some model is better than no model at all" the researcher had come to the conference to present their findings and to seek advice. (For the record I was there to talk about the more mundane subject of peat bogs.)

This project was an example of a common problem in applied mathematics: how do you model the collective behaviour of a large group of individuals? If those individuals are people, what sort of rules can we come up with to mimic the way individuals with free will can behave? This is far from an academic exercise. Many organisations rely for their operation on being able to control crowds safely and effectively. These include the police, the designers of sports stadia, London Underground, and even the Olympic Committee.

Agent based models (ABMs) provide one way of approaching this problem. In an ABM we have a number of agents (these could represent people or animals, for example) which interact with each other, and which often move under the effects of this interaction. Individual agents are typically assumed to be rational and to be acting in what they perceive as their own interest — such as reproduction, economic benefit, or social status. ABM agents may also learn from their experiences.

Most agent-based models are composed of:

• A number of agents
• A set of decision-making rules for each agent
• A set of learning rules for each agent
• A space in which the agents can move/operate and an environment in which they can interact.

Typically it's assumed that the agents' behaviour over time obeys a set of nonlinear ordinary differential equations: solving these equations will tell you where each agent will be at any given moment in time. However, the equations are much too complex to be solved by hand. Instead they are usually the subject of heavy duty computing.

The benefits of ABMs is that it is easy to change the parameters that describe the individual agents, which means that we can test, using computer simulations, how changes in the behaviour patterns of individual agents will affect the system's emerging overall behaviour. ABMs are now used widely in simulating systems in biology, sociology, economics and even management. We will now look at two examples.

Can you model a crowd of people? This is the Shibuya crossing in Tokyo, the busiest crossing in the world. Photo: Andrew Eland, CC BY-SA 2.0.

Modelling a crowd of people

Some of the fundamental work on the dynamics of crowds has been done by the Swiss mathematician and sociologist Dirk Helbing and his group. Helbing makes the following assumptions about the motion of people in a crowd:

1. They have an overall goal, which may be to exit a building or to follow signage
2. They cannot walk through walls or other solid obstacles
3. They have an (individual/cultural) view on how close they may want to be to strangers
4. They will want to be close to family or friends
5. They will have a certain amount of randomness in their movements.

The crowd is then modelled as a set of agents satisfying differential equations for their movement and what Helbing called a social force model for their interactions. Let’s assume that there are a total of people (agents) whom we will label , , and so on, up to . The position of a person is denoted by and their velocity by (for every ranging from  up to ).

The people are then subjected to three forces. The first is a global attractive force  acting on person and giving their intended motion. This motion is typically towards a target position at a desired speed  (which may evolve with time if the person is in a hurry for example). Between any to people  and there is also a repulsive/attractive force  that drives people to avoid/come closer other people. Between a person and some obstacle  there is a repulsive force  causing  to avoid .

For each person  the position  and the velocity  obey the differential equation

This just reflects the fact that the rate of change of position over time equals velocity.

We also have

This reflects the fact that the rate of change of a person’s position is determined by the sum of the three forces acting on them.

Finally, we have

where is a parameter which controls how quickly agent  reacts to a social situation: a small value of  means they react quickly (perhaps they don’t like crowds and walk away from them quickly) and a large value means they react slowly. The equation describes how the force , which gives a person’s intended motion, depends on the distance from their target and their desired speed.

To complete the picture we also have to model the social force . This is rather more complex to do and must take individual preferences into account. It requires information on the size and personal space preferences of each agent, and how well they can see and interact with other members of the crowd. Similar factors are used in the construction of the collision avoidance force . The expressions for the two forces are quite complicated and I won't attempt to give them in detail here. You can find out more in this paper by Helbing and Molnar.

Does it work?

Although we are dealing with that most complex of all things, human behaviour, the crowd models do quite a good job in predicting the actual behaviour of crowds. They have now been well tested in experiments, including one memorable experiment which involved photographing undergraduates in various crowded situations, behaving as naturally as they could given that they wearing different coloured hats for easy identification. You can see reports of some of these experiments in this paper by Helbing and colleagues.

As an example of how such models work, consider two large groups of pedestrians moving in opposite directions in a corridor (this often happens in train stations, for example, as the passengers of a train disembark and meet another group trying to embark). Below we see the simulation of this using the ABM social force model. On the top we see the distribution before the pedestrians meet, and on the bottom the pattern after the two groups have interacted. As you can see, the ABM has predicted that an emergent structure has arisen, with the pedestrians organising themselves into lanes. Next time you are in a similar situation have a look and see how true this is.

I have used these crowd models in the simulation, and design, of the lecture theatres at the University of Bath. In an earlier design of one of our lecture theatres we were faced with the prospect of over 300 students exiting a lecture theatre through a single entrance, whilst at the same time the same number of students are trying to get in. In the simulation below you can see the results, in which the two groups of students are coloured red and blue. The outcome of these computer experiments was that the door arrangements at Bath were impractical, and a new door was provided for the exiting students, which greatly eased the congestion.

Models of crowds are now finding widespread applications. In particular they have been used to model the Olympic stadium and many other sports stadia. They are also used by the London Underground and even by the organisers of pilgrimages. The objective of these models is to find safe ways of moving large numbers of people around in both normal situations and also in emergency situations such as a fire of a terrorist attack.

A recent and very profitable use of these has been in the movie industry. For example if a director wants to simulate a huge battle (of humans, or maybe trolls, orcs and elves) then it makes sense to use an agent based model to recreate the action.

Modelling birds

It is possible to model a flock of birds (and indeed a shoal of fish) in a similar way, but with some differences. One obvious difference is that birds and fish move in three dimensions (they fly or swim), and also that they have less of a long term goal or a wish to follow signage. They also want to avoid predators. In the case of modelling a flock, the individual agents are the birds and these agents obey a set of three rules:

• Alignment: Each bird aligns its flight with the average flight direction of the local flock (the part of the flock close to it)
• Cohesion: Each bird moves towards the average position of the local flock
• Separation: Each bird tries to avoid local over-crowding and predators.

These three rules, in varying strengths, combine to give emergent complex behaviours which can be simulated using an ABM. The rules are similar in many ways to those for crowds, but have the additional features of the three-dimensional motion. The best models also include the effect of the motion of the air around the birds. If this is all included, the resulting models closely resemble actual flock behaviour. The following simulation of a flock comes from a model created by Charlotte K. Hemelrijk and Hanno Hildenbrandt in their paper Some causes of the variable shape of flocks of birds. (The movie is reproduced here under a creative commons licence.)

There is now furious activity in improving these models and applying them to many other animal species.

Where next?

As I said in the introduction, one of the problems mathematicians are faced with is that of modelling Dorset. All we have to do is to identify the agents, which in this case are the many and varied local industries, together with the local population and the tourists, factor in the weather (and the effects of climate change), and predictions of the economy and the effects of Brexit, construct the rules of interaction, light the blue touch paper and hope that we get an answer which means something useful. Well we will try at least. Watch this space.

Chris Budd OBE is Professor of Applied Mathematics at the University of Bath, Vice President of the Institute of Mathematics and its Applications, Chair of Mathematics for the Royal Institution and an honorary fellow of the British Science Association. He is particularly interested in applying mathematics to the real world and promoting the public understanding of mathematics.

He has co-written the popular mathematics book Mathematics Galore!, published by Oxford University Press, with C. Sangwin, and features in the book 50 Visions of Mathematics ed. Sam Parc.

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Elaboration Likelihood Model

 

Having More Sex Doesn't Make Couples Any Happier; It's The Quality, Not Frequency, That Counts

 

By Samantha Olson

Let’s talk about sex. Are you having enough of it to make you and your spouse happy? Because it turns out frequency might not even matter. A team of researchers from Carnegie Mellon University studied how sex plays a role in our levels of happiness, and found frequency can make or break the mood. The study, published in the Journal of Economic Behavior & Organization, found a busy bed doesn’t mean a happy marriage.

Researchers randomly assigned sex frequencies to 64 healthy married couples between the ages of 35 and 65.  Half of the participants were nominated to have twice as much sex as they normally would in a week while the other half maintained the same pace, at their leisure, for three months. Each person was required to complete three different surveys throughout the experiment, with questions asking about a range of things, including health behaviors, happiness levels, frequency of sex, and types of sexual pleasures.

Participants weren’t happier the more they did the deed. In fact, they were less happy. The researchers explained this by saying an increase in sex frequency led to a decline in desire and enjoyment. In other words, it was the experiment's fault. 

"Perhaps couples changed the story they told themselves about why they were having sex from an activity voluntarily engaged in to one that was part of a research study,” said the study’s lead author George Loewenstein, from Herbert A. Simon University Professor of Economics and Psychology, according to CBS News. “If we ran the study again, and could afford to do it, we would try to encourage subjects into initiating more sex in ways that put them in a sexy frame of mind, perhaps with babysitting, hotel rooms, or Egyptian sheets, rather than directing them to do so."

Human beings were created to want to have sex because their biology is dictated by the need to procreate, according to the Harvard Business Review. Our instincts drive us to the bedroom, where women find a mate with quality genes and men find a mate healthy enough to bear a child. From an evolutionary standpoint, instincts naturally kick in to increase frequency, and neurohormones such as oxytocin and vasopressin are released, flooding the body with happy feelings that keep us wanting more.

The study may have put a damper on the sexual impetus. All of a sudden, participants weren’t drawn to each other with the primal urge to procreate, but instead to follow through on their assignment by increasing the frequency of intercourse. Sexual satisfaction relies more on the quality of sex than the quantity, as it relies more on those happy hormones flowing without pressure than tallying up notches on a bedpost.

"The desire to have sex decreases much more quickly than the enjoyment of sex once it's been initiated," said Tamar Krishnamurti, a research scientist in CMU's Department of Engineering and Public Policy, who designed the study. “Instead of focusing on increasing sexual frequency to the levels they experienced at the beginning of a relationship, couples may want to work on creating an environment that sparks their desire and makes the sex that they do have even more fun.”