Published on July 3, 2021 5:10 PM GMT

It seems to me that many people have intuitions in the direction of "it's extremely hard to know with any confidence anything about the eventual consequences of our actions". The place these intuitions are coming from provides some support for at least two problems for trying to do good in the world:

(1) Maybe we just have so little idea that even in principle the idea of trying to choose actions aiming at getting good eventual consequences is misguided.

(2) The massive amounts of uncertainty around consequences mean that doing good is a very hard problem, and that a key part of pursuing it well is finding strategies which are somewhat robust to this uncertainty.

In some sense (2) is a weaker version of the concern (1), and it only looks attractive to address conditional on concern (1) not biting.

What should these be called? I think (1) is almost always called cluelessness, and (2) is sometimes called cluelessness, but it seems like it would be helpful to have distinct terms to refer to them. Also on my perspective (1) is a reasonable thing to worry about but it looks like the concern ultimately doesn't stand up, whereas I think that (2) is perhaps the central problem for the effective altruist project, so I'm particularly interested in having a good name for (2).

Published on July 3, 2021 5:10 PM GMT

It seems to me that many people have intuitions in the direction of "it's extremely hard to know with any confidence anything about the eventual consequences of our actions". The place these intuitions are coming from provides some support for at least two problems for trying to do good in the world:

(1) Maybe we just have so little idea that even in principle the idea of trying to choose actions aiming at getting good eventual consequences is misguided.

(2) The massive amounts of uncertainty around consequences mean that doing good is a very hard problem, and that a key part of pursuing it well is finding strategies which are somewhat robust to this uncertainty.

In some sense (2) is a weaker version of the concern (1), and it only looks attractive to address conditional on concern (1) not biting.

What should these be called? I think (1) is almost always called cluelessness, and (2) is sometimes called cluelessness, but it seems like it would be helpful to have distinct terms to refer to them. Also on my perspective (1) is a reasonable thing to worry about but it looks like the concern ultimately doesn't stand up, whereas I think that (2) is perhaps the central problem for the effective altruist project, so I'm particularly interested in having a good name for (2).

Published on July 3, 2021 9:47 PM GMT

Context

My job is about helping people get involved in effective altruism, so I pay attention to how this tends to happen. I read every Tweet that mentions EA, and I see testimonials from many of the people who join Giving What We Can.

I'm not sure I've ever seen any piece of content not named "Doing Good Better" drive as much positive engagement as Sam Harris's two podcast episodes with Will MacAskill — especially the second one.

After the first episode, Sam was deeply affected, and pledged to donate $3500/month in podcast proceeds to the Against Malaria Foundation.

After the second episode, Sam joined Giving What We Can and pledged 10% of profits from his Waking Up app (well over $3500/month, I assume) to effective charities.

Impact

The second podcast seems to have caused a spike in GWWC memberships and in EA engagement more generally. Some notes on that:

GWWC doesn't have data on where people heard about it who signed up pre-2017 (which may leave out almost all of the signups from the first episode). However, GWWC estimates that ~700 people have taken a pledge in part because of that episode alone (half the main pledge, half Try Giving). To break this down:

~800 people who finished the sign-up survey mentioned a podcast as one way they found GWWC (the average person chose 1.8 sources)

Of the 123 people who said which podcast it was, 107 chose Sam Harris (87%)

Extrapolating a similar rate to the ~700 who didn't say which podcast gives another ~600 referrals

I didn't get an estimate on the impact of the first episode from GWWC, but it's plausible that hundreds of additional people may have come in through there (this probably depends mostly on how Sam's audience grew between 2016 and 2020).

Sam mentions at the start of the second episode that he's referred "hundreds of people", though this combines the first episode and the initial launch of the second Will conversation on his meditation app, Waking Up.

Anyone who found the conversation through the app would be included in the ~700 estimate above, as would some of the people who heard the first episode.

An extremely engaged community builder told me in February 2021: "I feel like most new EAs I've met in the last year came in through Sam Harris."

My subjective impression in the weeks after the second episode came out was that most of the ambient "positive EA chatter" I heard on Twitter (people tweeting out random EA endorsements who normally talked about other things) included mentions of the podcast.

Why was this so impactful?

My speculation on the important factors at play:

Sam set an example.

One of the most persuasive ways to promote something is to do it yourself.

One of Sam's explicit goals on the podcast is to get listeners to make ethical decisions, and I'd imagine that many listeners seek him out for ethical advice. This isn't as much the case for podcasters like Tim Ferriss or Joe Rogan, or other sources of publicity (TED, op-eds, etc.)

From the transcript below: "The question that underlies all of this, really, is: How can we live a morally beautiful life? That is more and more what I care about, and what the young Will MacAskill is certainly doing."

Sam made a rare endorsement.

Sam took several minutes to explain why he thinks giving is important, and gives GWWC a strong recommendation. This is a rare thing for him to do; most of his guests aren't selling anything (save maybe a book), and he doesn't advertise on his podcast.

Comparatively, Tim Ferriss (another major podcaster who had Will as a guest) has ~5 minutes of long-form advertising on every episode, and generally recommends lots of things every time a guest comes on. On the writeup of Will's episode, GWWC was the 23rd item on a bullet list of "selected links".

Tim's podcast referred 8 people to GWWC. This is actually a solid number, given that the "where you heard about us" question wasn't added until more than a year after that episode came out. But I think the tru...

Published on July 3, 2021 9:47 PM GMT

Context

My job is about helping people get involved in effective altruism, so I pay attention to how this tends to happen. I read every Tweet that mentions EA, and I see testimonials from many of the people who join Giving What We Can.

I'm not sure I've ever seen any piece of content not named "Doing Good Better" drive as much positive engagement as Sam Harris's two podcast episodes with Will MacAskill — especially the second one.

After the first episode, Sam was deeply affected, and pledged to donate $3500/month in podcast proceeds to the Against Malaria Foundation.

After the second episode, Sam joined Giving What We Can and pledged 10% of profits from his Waking Up app (well over $3500/month, I assume) to effective charities.

Impact

The second podcast seems to have caused a spike in GWWC memberships and in EA engagement more generally. Some notes on that:

GWWC doesn't have data on where people heard about it who signed up pre-2017 (which may leave out almost all of the signups from the first episode). However, GWWC estimates that ~700 people have taken a pledge in part because of that episode alone (half the main pledge, half Try Giving). To break this down:

~800 people who finished the sign-up survey mentioned a podcast as one way they found GWWC (the average person chose 1.8 sources)

Of the 123 people who said which podcast it was, 107 chose Sam Harris (87%)

Extrapolating a similar rate to the ~700 who didn't say which podcast gives another ~600 referrals

I didn't get an estimate on the impact of the first episode from GWWC, but it's plausible that hundreds of additional people may have come in through there (this probably depends mostly on how Sam's audience grew between 2016 and 2020).

Sam mentions at the start of the second episode that he's referred "hundreds of people", though this combines the first episode and the initial launch of the second Will conversation on his meditation app, Waking Up.

Anyone who found the conversation through the app would be included in the ~700 estimate above, as would some of the people who heard the first episode.

An extremely engaged community builder told me in February 2021: "I feel like most new EAs I've met in the last year came in through Sam Harris."

My subjective impression in the weeks after the second episode came out was that most of the ambient "positive EA chatter" I heard on Twitter (people tweeting out random EA endorsements who normally talked about other things) included mentions of the podcast.

Why was this so impactful?

My speculation on the important factors at play:

Sam set an example.

One of the most persuasive ways to promote something is to do it yourself.

One of Sam's explicit goals on the podcast is to get listeners to make ethical decisions, and I'd imagine that many listeners seek him out for ethical advice. This isn't as much the case for podcasters like Tim Ferriss or Joe Rogan, or other sources of publicity (TED, op-eds, etc.)

From the transcript below: "The question that underlies all of this, really, is: How can we live a morally beautiful life? That is more and more what I care about, and what the young Will MacAskill is certainly doing."

Sam made a rare endorsement.

Sam took several minutes to explain why he thinks giving is important, and gives GWWC a strong recommendation. This is a rare thing for him to do; most of his guests aren't selling anything (save maybe a book), and he doesn't advertise on his podcast.

Comparatively, Tim Ferriss (another major podcaster who had Will as a guest) has ~5 minutes of long-form advertising on every episode, and generally recommends lots of things every time a guest comes on. On the writeup of Will's episode, GWWC was the 23rd item on a bullet list of "selected links".

Tim's podcast referred 8 people to GWWC. This is actually a solid number, given that the "where you heard about us" question wasn't added until more than a year after that episode came out. But I think the tru...

Published on July 3, 2021 11:03 AM GMT

Doing good things is hard.

We’re gonna look at some deep tensions that attach to trying to do really good stuff. To keep it relatable(?!), I’ve included badly-drawn animals.

The mole pursues goals which are within comprehension and reach. At best the mole knows the immediate challenges extremely well and does a great job at dealing with them. At worst, the mole is digging in a random direction.

The giraffe looks into the distance, focusing on the big picture, and perhaps on challenges that will come up later but aren’t even apparent today. At best the giraffe identifies crucial directions to steer in. At worst, the giraffe doesn’t look where they’re going and trips over, or has ideas which are dumb because they don’t engage with details.

Moles have much more direct feedback loops than giraffes, so it’s harder to be a good giraffe than a good mole. When there’s a well-specified achievable goal, you can set a mole at it. Consequently many industries are structured with lots of mole-shaped roles. Idealists are often giraffes.

The beaver is industriously focused on the task at hand. The beaver rejects distractions and gets s done. At best, they are extremely productive. At worst, they miss big improvements in how they could go about things, or execute on a subtly wrong version of the task that misses most of the value.

The elephant is always asking how things are going, and whether the task is the right one. At their best, the elephant reorients things in better directions or finds big systemic improvements. At their worst, the elephant fails to get anything done because they can’t settle on what they’re even trying to do.

The mole and beaver are cousins, as are the giraffe and elephant. But you certainly get mole-elephants (applying lots of meta but only to local goals), or giraffe-beavers (just focused on the object-level of the big-picture).

The owl is a perfectionist. They have high standards for things, and want everything to meet those. At their best, they make things excellent, and perfectly crafted. If you want to produce the best X in the world, you probably need at least one owl involved. At their worst, they stall on projects because there’s something not quite right that they can’t see how to fix, and it’s unsatisfying.

The hare likes to ship things. They feel urgency all the time, and hate letting the perfect be the enemy of the good. At their best, they just make things happen! The hare can also be a good learner because they charge at things — sometimes they bounce off and get things wrong, but they get loads of experience so loads of chances to learn. At their worst, the hare produces loads of stuff but it’s all junk.

The dog is very socially governed / approval-seeking. They are excited to do things that people (particularly the cool people) will think are cool. At their best, they provide a social fabric which makes coordination simple (if someone else wants a thing done, they’re happy to do it without slowing everything down by making sure they understand the deep reasons why it’s desired). They also make sure gaps are filled — they jump onto projects and help out with things, or pick up balls that everyone agrees are important. At their worst, they chase after hype and status without providing any meaningful checks on whether the ideas they’re following are actually good.

The cat doesn’t give tuppence for what anyone else thinks. They’re just into working out what seems good and going for it. All new ideas involve at least a bit of cat. At their best, cats head into the wilderness and bring back something amazing. At their very worst they do something stupid and damaging that proper socialisation would have stopped. The more normal cat failure modes are to wander too far from consensus and end up working on things that don’t matter (where consensus would have made it easier to see that they didn’t matter), or to not know how to bring their catch back to the pack, so hav...

Published on July 3, 2021 11:03 AM GMT

Doing good things is hard.

We’re gonna look at some deep tensions that attach to trying to do really good stuff. To keep it relatable(?!), I’ve included badly-drawn animals.

The mole pursues goals which are within comprehension and reach. At best the mole knows the immediate challenges extremely well and does a great job at dealing with them. At worst, the mole is digging in a random direction.

The giraffe looks into the distance, focusing on the big picture, and perhaps on challenges that will come up later but aren’t even apparent today. At best the giraffe identifies crucial directions to steer in. At worst, the giraffe doesn’t look where they’re going and trips over, or has ideas which are dumb because they don’t engage with details.

Moles have much more direct feedback loops than giraffes, so it’s harder to be a good giraffe than a good mole. When there’s a well-specified achievable goal, you can set a mole at it. Consequently many industries are structured with lots of mole-shaped roles. Idealists are often giraffes.

The beaver is industriously focused on the task at hand. The beaver rejects distractions and gets s done. At best, they are extremely productive. At worst, they miss big improvements in how they could go about things, or execute on a subtly wrong version of the task that misses most of the value.

The elephant is always asking how things are going, and whether the task is the right one. At their best, the elephant reorients things in better directions or finds big systemic improvements. At their worst, the elephant fails to get anything done because they can’t settle on what they’re even trying to do.

The mole and beaver are cousins, as are the giraffe and elephant. But you certainly get mole-elephants (applying lots of meta but only to local goals), or giraffe-beavers (just focused on the object-level of the big-picture).

The owl is a perfectionist. They have high standards for things, and want everything to meet those. At their best, they make things excellent, and perfectly crafted. If you want to produce the best X in the world, you probably need at least one owl involved. At their worst, they stall on projects because there’s something not quite right that they can’t see how to fix, and it’s unsatisfying.

The hare likes to ship things. They feel urgency all the time, and hate letting the perfect be the enemy of the good. At their best, they just make things happen! The hare can also be a good learner because they charge at things — sometimes they bounce off and get things wrong, but they get loads of experience so loads of chances to learn. At their worst, the hare produces loads of stuff but it’s all junk.

The dog is very socially governed / approval-seeking. They are excited to do things that people (particularly the cool people) will think are cool. At their best, they provide a social fabric which makes coordination simple (if someone else wants a thing done, they’re happy to do it without slowing everything down by making sure they understand the deep reasons why it’s desired). They also make sure gaps are filled — they jump onto projects and help out with things, or pick up balls that everyone agrees are important. At their worst, they chase after hype and status without providing any meaningful checks on whether the ideas they’re following are actually good.

The cat doesn’t give tuppence for what anyone else thinks. They’re just into working out what seems good and going for it. All new ideas involve at least a bit of cat. At their best, cats head into the wilderness and bring back something amazing. At their very worst they do something stupid and damaging that proper socialisation would have stopped. The more normal cat failure modes are to wander too far from consensus and end up working on things that don’t matter (where consensus would have made it easier to see that they didn’t matter), or to not know how to bring their catch back to the pack, so hav...

Published on June 30, 2021 7:53 AM GMT

Motivation

During the last academic year, I decided to pause my studies at university and do some self-studies instead. I am a German law student and interested in AI governance. Before, I had already read quite some articles on the topic with the EA-Berlin reading group. However, I often found that a lack of technological knowledge made it difficult to fully understand the papers. Words like “algorithms”, “machine learning”, “neural networks” etc. didn't mean much to me. Even worse, the mere appearance of a mathematical sign like Σ was able to totally confuse me. So I decided to dedicate several months to closing some of these gaps. And as I imagine other people are in a similar situation, I would like to share my approach and some insights.

I think it is quite obvious that it makes sense for non-tech people to familiarize themselves with the technological aspects of AI, if they want to pursue a research or policy career in the field of AI governance. This enables them to understand the topic more fully, come up with more adequate and creative ideas, and communicate more easily with technological experts. The question to me is more about how deep people should go. I will leave that open for debate (I‘d love to see people discuss this in the comments!), as I am very uncertain about it. Personally, I just went as far as I could get until at some point I felt that it would take a lot of effort to get more insights, and I probably had picked the low-hanging fruits.

So let me take you with me through my journey. I'll link the resources I used and the courses I attended. Almost all of them are for free. But first, I'll briefly tell you what I didn't do and why.

What I didn't do

Initially, I considered studying computer science at a university for one semester. However, after looking at the syllabus and showing it to my friends with a technological background, it soon became clear that this wasn't a good idea. Usually, during the first semesters in computer science, a lot of basic mathematics as well as some computer hardware knowledge are taught. Most of this is not relevant for a high-level understanding of AI.

Another option was to work through a textbook on AI. Artificial Intelligence – A Modern Approach is a very popular one by Stuart Russel and Peter Norvig, which has just been updated last year. However, this book has more than a thousand pages and although I can be quite determined, I doubted that I could motivate myself to just sit at a desk with a book for months. For someone who can, maybe this is a good option – I don't know, since I ended up not working with the book at all.

What I did

Note: These are the resources I used, and I think they’re all good enough to recommend, but there may be better options. Please let me know in the comments if you’d recommend other resources!

Overview

To get some overview over the topic and some basic terms (like the difference between AI and ML), I recommend reading Understanding AI Technology, a Department of Defense resource for non-technical military personnel. It's not outstanding, and there are surely better overviews, but it covers very basic knowledge and I found it helpful. The second resource I highly recommend is Machine Learning for Humans, a series of blog posts that goes more in depth. If you don't have time for anything else that follows, maybe you can just read this. It is not possible (and at least if you continue the journey not necessary) to fully understand everything these resources talk about just by reading them though. Also, there probably are lots of other good introductory articles – these are the ones I read and found most helpful.

Now, finally, we can really get started. But where? Well, I was very fortunate to be pointed at this amazing website by a friend: How to Learn Machine Learning (in the following referred to as “the original website”). And what I did subsequently was to follow their steps with some adjustments....

Published on June 30, 2021 7:53 AM GMT

Motivation

During the last academic year, I decided to pause my studies at university and do some self-studies instead. I am a German law student and interested in AI governance. Before, I had already read quite some articles on the topic with the EA-Berlin reading group. However, I often found that a lack of technological knowledge made it difficult to fully understand the papers. Words like “algorithms”, “machine learning”, “neural networks” etc. didn't mean much to me. Even worse, the mere appearance of a mathematical sign like Σ was able to totally confuse me. So I decided to dedicate several months to closing some of these gaps. And as I imagine other people are in a similar situation, I would like to share my approach and some insights.

I think it is quite obvious that it makes sense for non-tech people to familiarize themselves with the technological aspects of AI, if they want to pursue a research or policy career in the field of AI governance. This enables them to understand the topic more fully, come up with more adequate and creative ideas, and communicate more easily with technological experts. The question to me is more about how deep people should go. I will leave that open for debate (I‘d love to see people discuss this in the comments!), as I am very uncertain about it. Personally, I just went as far as I could get until at some point I felt that it would take a lot of effort to get more insights, and I probably had picked the low-hanging fruits.

So let me take you with me through my journey. I'll link the resources I used and the courses I attended. Almost all of them are for free. But first, I'll briefly tell you what I didn't do and why.

What I didn't do

Initially, I considered studying computer science at a university for one semester. However, after looking at the syllabus and showing it to my friends with a technological background, it soon became clear that this wasn't a good idea. Usually, during the first semesters in computer science, a lot of basic mathematics as well as some computer hardware knowledge are taught. Most of this is not relevant for a high-level understanding of AI.

Another option was to work through a textbook on AI. Artificial Intelligence – A Modern Approach is a very popular one by Stuart Russel and Peter Norvig, which has just been updated last year. However, this book has more than a thousand pages and although I can be quite determined, I doubted that I could motivate myself to just sit at a desk with a book for months. For someone who can, maybe this is a good option – I don't know, since I ended up not working with the book at all.

What I did

Note: These are the resources I used, and I think they’re all good enough to recommend, but there may be better options. Please let me know in the comments if you’d recommend other resources!

Overview

To get some overview over the topic and some basic terms (like the difference between AI and ML), I recommend reading Understanding AI Technology, a Department of Defense resource for non-technical military personnel. It's not outstanding, and there are surely better overviews, but it covers very basic knowledge and I found it helpful. The second resource I highly recommend is Machine Learning for Humans, a series of blog posts that goes more in depth. If you don't have time for anything else that follows, maybe you can just read this. It is not possible (and at least if you continue the journey not necessary) to fully understand everything these resources talk about just by reading them though. Also, there probably are lots of other good introductory articles – these are the ones I read and found most helpful.

Now, finally, we can really get started. But where? Well, I was very fortunate to be pointed at this amazing website by a friend: How to Learn Machine Learning (in the following referred to as “the original website”). And what I did subsequently was to follow their steps with some adjustments....

Published on June 28, 2021 2:14 PM GMT

This post is focused on the UK Parliament, though hopefully some of its conclusions will be relevant to other similar systems (e.g. Australia, other parliamentary systems). I am not a Member of Parliament, but I have worked in Parliament for various MPs and I now work with them as a lobbyist for an NGO, which includes running an All-Party Parliamentary Group (APPG).

I wrote a similar post for those interested in working for an MP, which I recommend as a great first step Note also various posts on working in Congress and 80k's articles on UK politics and getting elected to Congress.

The objective of this post is to inform readers about:

What being an MP involves

How it can have an impact

What experience and skill set are required or desirable

What the risks are (personal and wider)

My general view is that more EAs should consider working in elected politics, as this seems to be a route with potential for high impact. Even if it turns out that the risks outweigh the benefits, it seems worthwhile for some members of the community to pursue it, and for many more to at least consider whether they might be suitable. Very keen for comments and feedback - especially where and when my comments on the UK context might be applicable or differ from other countries' systems.

What kind of roles are available?

There are various elected roles available in the UK, including:

Member of Parliament (MP)

Member of a devolved legislature - e.g. Member of the Welsh Assembly (AM)

Mayor of a city region - e.g. London

Local councillor

Police and Crime Commissioner (PCC)

Some legislature members are appointed rather than elected, namely:

Members of the House of Lords

This post, however, focuses solely on Members of Parliament, who sit in the House of Commons. This is generally the most senior and impactful of the above roles.

MPs can be further divided into the following roles:

Prime Minister / Leader of the Opposition

Secretary of State / shadow

Minister (junior) / shadow

Parliamentary Private Secretary (PPS) / shadow

Party spokesperson

Backbencher (an MP without a formal role in their party)

Select committee chair / member

The senior roles listed - Prime Minister and Secretary of States, followed by ministers - are generally the most impactful roles. Much of the expected value of becoming an MP will arise from having a chance of obtaining one of these roles.

The roles are not entirely mutually exclusive - select committee chairs and members are typically backbenchers, though MPs in smaller parties may have formal roles while also sitting on select committees.

What does the role involve?

Members of Parliament are elected by voters in a parliamentary constituency to represent that area. They tend to also be members of political parties. Their work is therefore divided between (1) duties arising from their constituency, (2) duties arising from their party, and (3) roles within the House of Commons. The kinds of task arising from these three areas are outlined in more detail below.

(1) Constituency

Managing casework - providing support to individual constituents’ issues, such as communicating with housing authorities, assisting visa applications or legal cases.

Responding to policy letters from constituents - either lobbying on their behalf, or explaining individual or party policy to them.

Running constituency surgeries - events (up to once a week) where constituents can alert the MP to individual issues or lobby them on policy.

Raising local issues in Parliament - a wide range of issues, e.g. the closure of a local train station, local homelessness statistics, investment in flooding infrastructure, gang violence, the impact of stamp duty on young people, the impact of pensions policy on elderly constituents.

Attending constituency events, such as primary school assemblies or the opening of a new hospital.

(2) Party

Formal role: MPs may have a specific role in the parliamentary party, such as being a minister, sh...

Published on June 28, 2021 2:14 PM GMT

This post is focused on the UK Parliament, though hopefully some of its conclusions will be relevant to other similar systems (e.g. Australia, other parliamentary systems). I am not a Member of Parliament, but I have worked in Parliament for various MPs and I now work with them as a lobbyist for an NGO, which includes running an All-Party Parliamentary Group (APPG).

I wrote a similar post for those interested in working for an MP, which I recommend as a great first step Note also various posts on working in Congress and 80k's articles on UK politics and getting elected to Congress.

The objective of this post is to inform readers about:

What being an MP involves

How it can have an impact

What experience and skill set are required or desirable

What the risks are (personal and wider)

My general view is that more EAs should consider working in elected politics, as this seems to be a route with potential for high impact. Even if it turns out that the risks outweigh the benefits, it seems worthwhile for some members of the community to pursue it, and for many more to at least consider whether they might be suitable. Very keen for comments and feedback - especially where and when my comments on the UK context might be applicable or differ from other countries' systems.

What kind of roles are available?

There are various elected roles available in the UK, including:

Member of Parliament (MP)

Member of a devolved legislature - e.g. Member of the Welsh Assembly (AM)

Mayor of a city region - e.g. London

Local councillor

Police and Crime Commissioner (PCC)

Some legislature members are appointed rather than elected, namely:

Members of the House of Lords

This post, however, focuses solely on Members of Parliament, who sit in the House of Commons. This is generally the most senior and impactful of the above roles.

MPs can be further divided into the following roles:

Prime Minister / Leader of the Opposition

Secretary of State / shadow

Minister (junior) / shadow

Parliamentary Private Secretary (PPS) / shadow

Party spokesperson

Backbencher (an MP without a formal role in their party)

Select committee chair / member

The senior roles listed - Prime Minister and Secretary of States, followed by ministers - are generally the most impactful roles. Much of the expected value of becoming an MP will arise from having a chance of obtaining one of these roles.

The roles are not entirely mutually exclusive - select committee chairs and members are typically backbenchers, though MPs in smaller parties may have formal roles while also sitting on select committees.

What does the role involve?

Members of Parliament are elected by voters in a parliamentary constituency to represent that area. They tend to also be members of political parties. Their work is therefore divided between (1) duties arising from their constituency, (2) duties arising from their party, and (3) roles within the House of Commons. The kinds of task arising from these three areas are outlined in more detail below.

(1) Constituency

Managing casework - providing support to individual constituents’ issues, such as communicating with housing authorities, assisting visa applications or legal cases.

Responding to policy letters from constituents - either lobbying on their behalf, or explaining individual or party policy to them.

Running constituency surgeries - events (up to once a week) where constituents can alert the MP to individual issues or lobby them on policy.

Raising local issues in Parliament - a wide range of issues, e.g. the closure of a local train station, local homelessness statistics, investment in flooding infrastructure, gang violence, the impact of stamp duty on young people, the impact of pensions policy on elderly constituents.

Attending constituency events, such as primary school assemblies or the opening of a new hospital.

(2) Party

Formal role: MPs may have a specific role in the parliamentary party, such as being a minister, sh...