Tag Archives: news

Game Dev Update: Twisty little passages, all alike

A quick update this time — I’ve been working here and there on implementing some maze generation code for the game, so that I could have a few different types of dungeon levels to generate.  At last I’ve got it working, and can now generate some intimidating mazes using the ‘growing tree’ algorithm:

maze16

By altering the likelihood of new branches in the path, I can change the feel of the maze significantly.  The maze above has a high likelihood of producing new branches; the one below produces much longer hallways:

maze19

 

Tonight I’ve just added a variation of this algorithm which mimics another well-known maze generation method, the ‘recursive backtracker’.  This one needs some fine-tuning, though, as currently it produces very long, meandering corridors that can be a little annoying to navigate:

maze20

The next step is to make the maze generator a bit more flexible.  Ultimately what I’d like to do is allow the normal dungeon generator to create maze rooms which can be integrated into the rest of the dungeon.  This will add some more variety in the dungeon without forcing the player to navigate an entire level-spanning maze every time the dungeon generator decides to mix things up.

I do think I want to have one dungeon level that’s entirely a maze, though, and encourage exploration by sticking a powerful artifact somewhere within and dropping some hints that the player might find it if they have a look around.  I’ll also scatter some Scrolls of Clairvoyance about, which will reveal the location of the level exit and make navigating the maze less directionless.

As you might’ve guessed, in these screenshots I’ve switched off the ‘fog of war’ for the player so that I could observe and test the results of the maze generator.  In actual play things look more like this:

maze17

By way of comparison, here’s how a maze level looks in the famous(ly difficult) roguelike Nethack:

Image result for nethack gehennom

More to come next time, when hopefully I’ll have maze rooms working.

 

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Game Dev Update: Upping the Challenge

So it’s been a little bit since the last update on my hobbyist game development efforts, but a lot’s been going on whenever I can snatch some time in between the constant, endless presentations I’ve had to prepare for recently on the academic side of my life.

My focus in the last few weeks has been on some core gameplay systems.  Originally I was going to work on the dungeon environment, but I figured that fancy dungeons wouldn’t be that useful if the player couldn’t do interesting stuff in them, so I went for under-the-hood gameplay systems instead:

Hunger system

Hunger systems are a classic feature of roguelikes — since the original Rogue, in fact!  The idea is that the player needs to seek out food within the dungeon and eat it regularly, otherwise they gradually begin to starve to death.  Since food is limited and can only be found within the dungeon and not created by the player, it serves as a time pressure mechanism; the player has to keep moving further down the dungeon to find food in order to stay alive.  Currently my system is very basic: the player starts with three rations, and can find two different types of food items within the dungeon that refills their hunger meter.  If at any point your Satiety stat dips below 50, you start getting warnings, and at 0 begin taking damage every turn.  At -50 you’ll die of starvation.  I’ve added an indicator to the interface that shows this stat directly, unlike Rogue and some other games that keep it hidden and only warn you shortly before death:

satiety7

Turn Scheduling:

In order to make monster fighting more interesting, I wanted to add a system for variable attack and movement speeds between different creatures.  This is kind of a weird thing to implement in a turn-based game, and I wasn’t sure of the best way to go about it at first.  Eventually I settled on a central turn scheduling system in which all monsters (and the player) schedule their next turn each time they take an action.  The number of turns they have to wait until they can move or attack again depends on their Speed stat.

It sounds simple, but it turned out to be a big pain to get right!  I had a lot of weird bugs where certain monster turns didn’t register, or the game would suddenly stop running in turn-based mode and let monsters run rampant while the player was unable to move, and all sorts of other problems.  Eventually I got all that ironed out, and finally had fast-moving wolves and bats, slow-moving zombies, etc… only to find that in certain circumstances the player would encounter invisible and invincible enemies after moving to a new dungeon level.

After a few days of annoyance I worked out the problem — monsters were dying in combat, but a reference to them was remaining in the turn schedule, meaning that memory was still being allocated for their monster data by the program.  In certain situations that meant the scheduler would find that reference, say to itself ‘hey this here says there should be an orc doing something’ and the player would end up fighting a ghostly remnant of a previously defeated enemy!

I was able to fix that relatively easily once I figured it out — this is why we keep backups, kids.  Also it was a useful reminder to be more careful about my coding practices in some parts of the game, so I did quite a few bits of refactoring of the code to prevent anything like that happening again.

Combat System Changes:

Again in service of making combat more interesting I made some significant changes to the combat system to better differentiate enemies.  My favourite series of Japanese RPG games is the Shin Megami Tensei series, which are known for being set in a dark demon-infested version of Tokyo and for being really difficult.  What I love most about these games is that the combat system encourages tactical thinking by having weapons and spells deal a number of different types of damage, which demons can be weak to, immune to, or resistant to depending on their nature.

I implemented something similar to this, where each monster has weaknesses to certain types of physical damage (in this game they’re called Phys for general slashing attacks, Blunt for hammers/clubs, Pierce for arrows and spears, alongside numerous magical damage types like Fire, Ice, Thunder, etc.).  Hitting a monster’s weakness will do double damage to it, while hitting if it’s resistant you’ll deal only half damage.  Some monsters are immune to certain types of damage entirely — Skeleton Warriors, for example, aren’t bothered at all about being poked by arrows given their total lack of fleshy bits.

The idea is that this will push the player to experiment with different weapons, spells and items in order to dispatch enemies more quickly — particularly when they reach later dungeon levels, where monsters start appearing in large swarms and have powerful attacks.  I want damage types to be a major focus for the player, and for good attack choices to have significant rewards (and for bad choices to have a big impact!).

To make things a little easier for the player, I’ve added a Look command so that you can see some details about monsters in visual range:

look-command

Other than these big changes, mostly I’ve been squashing bugs and adding bits and pieces of content.  At this point, the player can face about 300 different monsters, collect 50 different weapons and items, and visit 15 different dungeon levels.  As time goes on I’ll keep adding new monster types, then randomly-generated weapons and items, and finally some super-tough boss monsters including a final boss.  At that point I think I’ll be ready for a playable alpha release to get some gameplay feedback.

This week has marked the official start of the academic year, so there’s been a ton of things to do at the university — which means there hasn’t been much time for game development!  I’ve been keeping careful track of all my additions and to-do lists and such, so when I have time to get back to things I’ll remember what I’d planned to do next.

All in all it’s a hell of a lot of fun so far, even when I’m getting frustrated by weird bugs.  I’m definitely gaining some major Python proficiency thanks to all this, and it’s forced me to tighten up my coding practices and embrace proper version control.

After some more work on damage types and attack variety I’ll be modifying the game UI and sprucing up the general look of things, so hopefully I’ll have some more interesting screenshots next time 🙂

I’ll leave you now with some game recommendations:

SDL Rogue: a well-done Windows port of the original Rogue, with some nice additional options (including a graphical tile option).  Type ‘?’ to see all the commands (there’s a lot of them!).  The same site also has similar ports of other classic roguelikes, including Hack (the predecessor to Nethack), Larn, and Moria (predecessor to Angband).  Check ’em out.

DoomRL:  This is a fantastic free game that combines two of my favourite things — the original Doom (in my opinion the greatest computer game of all time, and one I still play regularly), and roguelikes.  It masterfully combines frantic Doom-style demon-blasting with turn-based roguelike gameplay and character progression, and to top it off has the original Doom music and sound effects, and some fantastic 2D graphical tiles.  Download it!

Some enterprising fan has developed a server for DoomRL, too, so you can see how you stack up against other players and even play in your browser (or via a Telnet client).  ASCII text mode only though!

Crypt of the NecroDancer:  This game kicks my butt all over town, but it’s incredibly creative and fun.  It’s a turn-based roguelike with a twist: the entire game world is tied to the rhythm of the music for the level, and making your moves along with the beat gives you various bonuses (and moving out of time leaves you open to attack!).  All the items and monsters play on this theme.  The graphics are incredibly charming, the music is by Danny Baranowsky (of Binding of Isaac and Super Meat Boy fame) and is absolutely fantastic, and it’s packed to the brim with cool features and secrets to unlock.

It’s also 50% off on Steam right now, so now’s a great time to take the plunge!

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Funded PhD opportunity at Teesside

Fully-funded PhD opportunity available! I’m looking for someone interested in working on agent-based modelling for healthcare applications. No fees and £20K stipend. These are four-year positions and you will be asked to contribute up to six hours per week of teaching (tutorials/demonstration only, no lectures), which is more work but also good for the CV. Click here and filter under ‘Computer Science’ to see my project.  For more about me, check out the various pages on this blog or my staff profile at Teesside.

Project description: This research will focus on the application of Agent-Based Modelling techniques to human social systems, with particular emphasis on digital health applications. In the context of public health, agent-based models can help us understand the complexities of health policy implementation and service delivery by modelling the multiple interacting processes underlying the health system. These models will investigate challenges in health and social care service delivery across a variety of spatial and temporal scales – from short-term studies of demands on accident and emergency services, to longer-term explorations of the pressures facing social care over the next several decades. Our multi-disciplinary team will work with members of the School of Health and Social Care here at Teesside, along with external collaborators and stakeholders. The project would be suitable for a graduate with a background in Computer Science, Artificial Intelligence, Statistics or Complexity Science with an interest in Public Health/Healthcare applications.

ACADEMIC FRIENDS: Please tweet/share this as widely as you can!

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Paper accepted to Alife XV

I’m pleased to say that the paper I’ve been going on about now for some time, titled Job Insecurity in Academic Research Employment: An Agent-Based Model, has been accepted to Alife XV in Cancun this summer.  I’m currently working on some revisions to the paper to account for some helpful suggestions from the reviewers — as soon as the final camera-ready preprint is available I’ll post it here and the usual places (ResearchGate, Academia.edu, etc.).

Hope to see some of you in sunny Mexico come July 🙂

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Paper Submitted To Alife XV

I’m happy to report that I’ve recently submitted a first paper on the postdoc simulation I’ve been plugging on these pages for some time.  I’ve been working in collaboration with Nic Geard of the University of Melbourne and Ian Wood, my officemate at Teesside.

The submitted paper is titled Job Insecurity in Academic Research Employment: An Agent-Based Model.  Here’s the abstract:

This paper presents an agent-based model of fixed-term academic employment in a competitive research funding environment.  The goal of the model is to investigate the effects of job insecurity on research productivity.  Agents may be either established academics who may apply for grants, or postdoctoral researchers who are unable to apply for grants and experience hardship when reaching the end of their fixed-term contracts.  Results show that in general adding fixed-term postdocs to the system produces less total research output than adding half as many permanent academics.  An in-depth sensitivity analysis is performed across postdoc scenarios, and indicates that promoting more postdocs into permanent positions produces significant increases in research output.

The paper outlines our methodology for the model and analyses a number of different sets of scenarios.  Alongside the comparison to permanent academic hires mentioned above, we also look closely at unique aspects of the postdoc life cycle, such as the difficult transition into permanent employment and the stress induced by an impending redundancy.  For the sensitivity analysis we used a Gaussian process emulator, which allows us to gain some insight into the effects of some key model parameters.

The paper will be under review for the Alife XV conference very shortly, so I don’t want to pre-empt the conference by posting the full text here.  If — fingers crossed — it gets accepted, I’ll post a PDF as soon as it’s appropriate.  If you want a preview or are interested in collaborating on future versions of the model, please get in touch!

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Science about Science: Does Promoting More Postdocs Help?

Just a brief one today — I’ve been playing with parameter settings on the funding/careers model, particularly the impact of postdoc promotions.  In the base scenario, postdocs (referred to as PDRs here: Post-Doctoral Researchers) have about a 15% chance of getting promoted to a permanent position.  Here’s a sample run at the base settings (which includes the mentoring bonus added last time):

r_mean_pdr2

I’ve finally worked out how to fix the legends on these graphs!  Now let’s compare to a scenario in which 50% of postdocs get promoted:

r_mean_pdr2

 

Note that the mean productivity of grant-holders (the green line) is overall a bit higher than in the 15% case.  The productivity of promoted postdocs (the orange line) also tracks higher over time than in the 15% scenario.

Now let’s try 100% promotion chance:

r_mean_pdr2

Here the productivity of grant-holders and promoted PDRs is higher than in either the 50% case or the 15% case.

So does this mean that promoting more postdocs is our ticket to a more productive research community?  Well, in this virtual academia it seems to help — but still we’re seeing a lower level of productivity than in the postdoc-free scenario.  Not to mention that there’s still quite a bit of statistical work here to be done to determine how significant these effects are — but it’s an interesting result from today’s work and one I hope to address in the paper, assuming that the analysis bears it out.

 

 

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Science About Science: More Scenarios

Since my last post I’ve been doing a lot of work on cleaning up the simulation and adding some additional scenarios to the mix.  After some in-depth discussion with colleague Nic Geard, co-author of the 2010 academic funding model that inspired this work, we decided that a good starting point for this would be to compare a more basic growing population of permanent academics with a population that includes insecure postdocs.

So that led to me getting to work on re-working some things to allow for four possible scenarios:

  1. Core academic funding model as written by Nic and Jason
  2. Simple growing population of permanent academics
  3. Population which includes postdocs, in which research quality does not increase the chances of promotion for postdocs
  4. Population which includes postdocs, in which research quality does increase the chances of promotion for postdocs

This last scenario in particular is intended to investigate how things proceed if we have an optimistic view — we know exactly how good each postdoc is, and we hire only the very best 15% of the current crop during each iteration.  Those in favour of the current structure would most likely argue that competition for limited jobs allows the cream to rise to the top, so we need to investigate whether that assumption holds.

So for the purposes of this post, I’ve done a quick run of the sim for each of these scenarios.  Note that the previous model by Nic and Jason investigates the time-management aspect of grant applications much more deeply — right now I’m just focusing on the mean research output for different groups of academics under each scenario.

Scenario 1: Core Academic Funding Model

If you alter the parameter settings of my version of this model and turn off all my additions — growing populations, promotion mechanisms, and the postdoc system — you end up with a scenario that’s nearly identical to the original model by Nic and Jason.  The only major difference is that in my version the bonus in research quality given to grant-holders is 1.5 rather than 1.25.

So what we see is that grant-holders, as you might expect, have a massive advantage in terms of research productivity:r_mean

Grant-holders are sitting pretty at the top there, although their output fluctuates given that various researchers of differing levels of research talent are jumping in and out of the grant-holders club each semester.

(NB: I’m aware that the non-grant holders are invisible in this graph and the next — I’m working on it.  This is all a work-in-progress, it’ll get there in the end!)

Scenario 2: Growing Population

In the second scenario, I’ve added a mechanism which adds a few academics to the population each semester.  Their research quality and initial level of time investment into grant proposals is randomised.  As in the last post we’re living in a generous society here where research funding stays in step with the growing academic population — 30% of applicants are always funded, regardless of the population size.

Perhaps unsurprisingly, the results look nearly the same as in Scenario 1:

r_mean

Just like in Scenario 1, grant-holders do far, far better than the overall population, particularly those applicants whose grant applications have failed.

Scenario 3: Postdocs, Random Promotions

So now things start getting more bizarre.  In this scenario we introduce the postdoctoral system outlined in my previous posts.  Postdocs are added in proportion to the number of grants that have been funded in a given semester, with a bit of random variation to spice things up.  New postdocs are assigned contract lengths between 4 and 10 semesters.  For the first two semesters their research quality is lower to account for their adjustment period into a new post; similarly, their last two semesters also see a drop in quality due to the time they must devote to finding a new post.

At the end of their contract, postdocs have a 15% chance of being promoted into a permanent position.  That may sound harsh, but that’s actually slightly more generous than reality (the figures I’ve seen have it pegged at 12%).  Research track record doesn’t count in this scenario — this is a world where promotions are entirely a lucky coincidence (some would argue that this is broadly reflective of reality).  Once promoted, they’re now permanent academics and can apply for grants.

So here’s a sample run of the latest formulation of this scenario:

r_mean_pdr

Much like the last set of early results, we see a drastic drop in mean research output amongst permanent academics who are grant-holders, and postdocs don’t do very well in terms of productivity despite allocating 100% of their time to research.  Overall we see no benefit to research output of the population with the introduction of postdocs, and both permanent academics and postdocs see significant variability in their research output.  My interpretation is that the introduction of a randomised population of insecure researchers is massively disruptive — each semester we don’t know how good our postdocs will be, so their output is highly variable, and we also don’t know how good our promoted academics will be, so again we see fluctuations at that level too.

Scenario 4: Postdocs, Non-Random Promotions

This scenario is particularly intriguing to me.  Nic and I had wondered whether selecting only the very best postdocs from the crop for promotion each semester would improve the picture or not.  After all if we pick the best of the best and put them in a position to get grants and thus that juicy grant-holder output bonus, surely things will go much better for our virtual scientists?

Well… not massively:

r_mean_pdr

Now you’ll see in this run that actually both the grant-holders and postdocs appear to be doing a bit better in terms of research output.  Initially this seems good, but by the end of the simulation we see that the mean research productivity for the overall population is actually slightly lower than in the random promotions case!

At first blush this seems nonsensical, but if we ponder it for a moment I think it makes sense.  While the non-random promotions do mean that we get the best of the postdoc population promoted each semester, it still means we’re highly dependent on the whims of the random-number generator — if we get a few bad crops of postdocs, in other words, we just end up with more crappy academics, and our exacting knowledge of postdoc research quality hasn’t saved us from the disruptive influence of the constant influx of new people with highly variable research output and contract lengths.

Moreover, there’s no mechanism at present for postdocs to be mentored or to mature in their research abilities — once crappy, always crappy, in other words.  In real life people may argue that the trials and tribulations of postdoc life can allow young researchers to grow into more productive academics — so that’s another aspect we need to examine.

I’ve done a bunch more runs with different random seeds and seen variations in the output that seem to support these ideas, but I’m going to spare you the 18 other graphs.  Suffice to say that the graph above seems to indicate a lucky series of postdoc recruitment drives more than anything else.  Instead I’ll keep working at it and post more when I’m more clear on my interpretations of this scenario.

SURPRISE NEW SCENARIO: Non-Random Postdoc Promotions, With Mentoring Bonus!

Wow, what a day for you lucky people!  I’ve just decided to do a quick-and-dirty scenario where we give promoted postdocs in the non-random scenario a bonus to their research quality to attempt to simulate postdocs being mentored toward success by their superiors.  Surely we’ll see a change in the fortunes of our virtual scientists now?

Well… not really:

r_mean_pdr

In fact things look almost identical, with the exception of the overall mean research output hitting a plateau rather than dropping slightly at toward the end of the sim, as we saw above.

To be fair, however, the ‘mentoring bonus’ I gave out here was not outrageously large — effectively the promoted, mentored postdocs get a 25% bonus to research quality.  What if I double that to 50%, what do we get?

r_mean_pdr

Ah-ha!  At last, a very slight positive outcome.  Mean research output overall trends ever so slightly upward over the course of this simulation run, rather than plateauing or starting to fall as above.

But I think we’d have to admit here that this is a fairly minimal outcome considering a rather generous scenario — and it’s quite likely this won’t hold in every run and some other runs may show worse results depending on the feelings of the random-number generator.  It seems reasonably consistent at a quick glance — out of 10 runs I’ve just done for this scenario, 7 out of the 10 showed a similar tiny, tiny positive trend.

So what I’ve gathered from today’s work is that increasing the average research output of academics in a postdoc scenario requires some major work: we need to recruit only the very best postdocs; and we need to ensure they get mentoring of high enough quality that they are a full 50% better than they were during their postdoc days.  Even with these powerful tools, that’s still barely enough to overcome the disruptive impact of a fluctuating population of insecure overstressed young researchers.

In real life of course, we don’t have such a transparent method of evaluating research outputs and determining the best postdocs to hire — nor do we have a population of super-mentors who can massively improve the productivity of every single postdoc.  So, if we believe the underlying assumptions of this model, then perhaps we should start to think about whether insecure research posts are a good thing for science or not.

Of course there’s a human dimension here as well — over the many runs I’ve done with the postdoc mechanism running, most simulations top out around 500 active academics at the end of the simulation, and between 5-600 total postdocs hired over the 100 semesters.  Out of those we’ll see between 70-90 postdocs get promoted, while the rest all get the sack and leave academia forever.  Do we really want to be sending these vast numbers of PhD graduates out of the academy and lose all that potential research talent?  That seems like an incredible waste, and even more so when we see how difficult it is to get a positive impact on productivity out of this structure.

Next time: I’ll keep poking at this simulation and see whether these results hold up, and I’ll be doing some other comparisons on other measures, including total research output across different groups.  Early indicators: postdocs increase total research output, and research quality across the population becomes highly unstable.  More later.

 

 

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York Inequality Workshop, Part II

In my last post I summarised the morning session of the York inequality workshop I attended last week.  Today I’ll cover the main event of the day, the plenary session by Kate Pickett and Danny Dorling.

Kate is well known as one of the co-authors of The Spirit Level, a book about the many and varied impacts of inequality in society that received major publicity a few years ago.  Danny Dorling has written several books on the topic, including Inequality and the 1% and Unequal Health: The Scandal of Our Times.  They delivered the talk jointly, framing it as somewhat of a contrast — with Danny offering a fairly sobering perspective on inequality, followed by Kate with a slightly more optimistic picture.

Inequality in the UK

Danny opened by showing us some graphs which showed a worrying trend.  The National Health Service here in the UK tracks a statistic on its success in reducing premature death from preventable causes — it’s referred to as statistic 1A, perhaps the single most important indicator of the health service’s performance.  The graph showed that in recent years, progress has stalled on this all-important indicator.  This has coincided with a general rise in health inequality in the UK and ever-increasing economic inequality.

In terms of the broader picture, the UK is at the bottom of the league tables in terms of equality in Europe.  Infant mortality is among the highest in Europe — our figures are closer to Europe than to Sweden.  Our income inequality is the highest in Europe, with the best-off 10% of the population taking home 28% of the country’s income.

Danny argued that research shows income inequality has a disastrous effect on everyday life and culture in highly unequal countries.  People in unequal countries tend to trust each other less, and tend to think of other people as less deserving of help.  Social classes become stratified, and culture begins to separate along economic lines.  Health inequality gets more severe as economic inequality grows — and that leads to disturbing outcomes.  For example, here in the UK two times more children die each year than in Sweden, a country with much greater equality.

Do we care enough?

By way of demonstration, Danny presented us with a number of comments from GPs on a story about the growing number of requests from patients for referrals to food banks.  In comment after comment, GPs offered comments that were shockingly unconcerned about the fact that their patients found themselves unable to put food on the table.  These patients were described as irresponsible, their problems seen as medically irrelevant or simply not the GP’s responsibility — despite the very clear and obvious link between poverty and poor health.

Danny presented these as evidence that even amongst members of our society trained specifically to look after others, the predominant view in recent years is that there are a substantial portion of people who do not deserve our help.  We are inclined to see people around us as irresponsible or lazy, rather than victims of circumstance, even despite the evidence that the vast majority of people in poverty spend enormous amounts of time and energy trying to escape it.

He argued that this predominant mindset leads to a culture in which we simply don’t care enough about the circumstances of others, and as a consequence we don’t act to prevent unnecessary death and misery in our society.  He pointed to figures showing the link between economic inequality and traffic deaths — two times more children die crossing the street than in more equal societies like France, the Netherlands or Norway.  Deaths due to suicide or drug poisoning are also far higher in the UK.  Overall we have a much higher incidence of mental illness in the UK than in Europe, second only to the US.

The political view

Danny closed by discussing how these damaging views on equality in our society are promoted and perpetuated by those in power.  Statistics show that the UK spends less on a per-capita basis for healthcare than any comparable country — in some cases drastically less (on the order of 28-40% lower than most countries in Europe, and half or less the spend of some countries like Denmark).  The fact that the NHS is able to demonstrate as many good health outcomes as it does is remarkable, given how little we spend compared to our neighbours.

When we zoom out and look at state spending in the UK as a whole, the trend continues.  The current Conservative government is presiding over a drastic shrinking of the state, to a level not seen since 1918.  Children in private education have 4.5 times more money spent on them than state schoolkids.  Once again when we compare state spending on health and welfare in the UK as a proportion of the overall budget, we are way down at the bottom of the league table.

Yet despite all this, the current government continues to paint a picture of the UK as a country where state spending is out of control.  George Osborne tells us that we’re a reckless tax-and-spend country, painting a dire picture of overspending leading to a precarious economy that could collapse at any moment (despite so many experts disagreeing with both his assessment and his predictions of the consequences of the sovereign debt).

So, Danny asks, does Osborne and the rest of the government actually believe this?  Are they so steeped in this economic view that they fail to see the myriad statistics that show the opposite?  How do they fail to see that these merciless budget cuts, so often levelled at the poor, the sick, and the disabled, push us further down the road toward deep inequality that will damage our health and further divide our society?

Kate’s response — Is it as bad as all that?

After Danny’s presentation the mood in the room was understandably severe.  He painted a picture of severe and growing inequality in the UK, and a government that appears totally uninterested in addressing it.  With our own views seeming fundamentally warped by inequality, is it even possible that we can get things back on track?

Kate started off by saying that she was going to try to offer a more optimistic picture than Danny — but that in fact everything he said was right and she didn’t disagree fundamentally with any of it.

She started off by highlighting the issue of wealth inequality, which has been a topic of much greater interest in recent years due to movements such as Occupy Wall Street.  She showed some graphs confirming the stratospheric rise in the share of wealth going to the top 1% of society in the US and UK since 1980 — a direct consequence of the policies of the Reagan/Thatcher era.  Post-1985 we’ve also seen a massive rise in pay for CEOs relative to their employees — we’re now at a point where CEOs tend to make 300-400 times what their average employee makes.  The UK historically was much less bad than the US on this measure, but in recent years has caught up.

Rising awareness of inequality

Kate said that one positive aspect of this is that most of these facts and figures are by now quite familiar to many of us — in no small part due to the efforts of Occupy Wall Street and similar movements.  She argued that wealth inequality has become part of the conversation now, after the economic crisis.  She gave several examples of how wealth inequality is now a target for major charities, including Oxfam.

She pointed to a particular campaign from Oxfam which offered the statistic that the world’s richest 85 people hold the same wealth as the poorest 3.5 billion people.  As it turns out, they got the figures wrong and had to present a correction — in fact, the richest 83 people hold the same wealth as the poorest 3.5 billion.

As it happens, Oxfam has updated those numbers just a few hours ago — and things have become even worse.  Now the top 62 wealthiest people hold the same wealth as the poorest 3.6 billion people on Earth — the bottom 50%.

As far as the UK is concerned, Kate discussed the case of the Sustainable Development Goals panel at the United Nations.  This panel, of which the Prime Minister David Cameron was a member, was tasked with producing a series of key goals for all countries leading up to the year 2030.  Kate in her capacity as equality campaigner and co-founder of The Equality Trust wrote to all the world leaders on this panel to urge them to include reducing inequality as one of the development goals.  She received a positive response from every leader on the panel (including President Barack Obama) — except for David Cameron.  He delegated his response to one of his cabinet ministers, who told her that inequality is not a policy priority in the UK.

Fortunately the other world leaders overruled Cameron’s objections, and the Sustainable Development Goals explicitly include reducing inequality (see #10).  This whole scenario very much backs up Danny Dorling’s assertions about the UK government’s views on inequality — they seem more than happy to ignore the evidence of the impact of inequality and continue their efforts to widen these gaps in society.

Can we reduce the impact of inequality and greed?

As a way of offering a more positive perspective, Kate discussed an interesting study about the behaviour of wealthy people.  She highlighted the work of Paul K Piff, a social psychologist who studies the behaviour of the wealthy.  He found that higher social class is a strong predictor of unethical behaviour — in laboratory studies wealthy people are more likely than poorer people to break the law while driving, steal valuable items from others, lie during negotiations, and so on (see the 2012 PNAS paper).  One of his studies actually, seriously involved taking candy from children — and yes, the wealthy subjects were far more likely to do it.

Follow-up studies have shown something interesting, however — when the wealthy subjects are asked to ponder some facts and statistics regarding inequality before engaging in these tests, their behaviour becomes markedly more moderated.  They become less likely to make unethical decisions once they’re asked to keep those ideas in mind.

So, in Kate’s view, this means that there’s a real, demonstrable impact from spreading the word about the problems caused by inequality, and from making these ideas part of the public debate.  The more people ponder these ideas, the more they may moderate their own behaviour — and perhaps become motivated to start their own efforts to address the problem.  She noted the recent spread of Fairness Commissions in local authorities throughout the UK, and suggested that these are a consequence of far greater numbers of people contemplating inequality and wanting to take direct action to address it locally.

Conclusions and thoughts

As is often the case with these kinds of presentations, I came away from the session feeling rather overwhelmed, exhausted and depressed — despite already knowing most of these figures.  There’s something about being shown the whole grim picture at once that makes it feel like a real gut-punch of hopelessness and despair.  All I could think was how powerless we all seem to be to stop the endless march toward inequality and division, how the entire power structure of the world seems oriented toward consolidating wealth and power at the very top of society while the rest of us are left with poverty and desperation.

In that respect I appreciated Kate’s perspective — she did offer some hope by presenting a possible future in which keeping inequality in the public conversation leads to changes in our behaviour, which eventually will be reflected in the structure of our societies.  She showed us that world leaders — well, except for David Cameron — do consider inequality a problem at least on some level, and are willing to commit to addressing it in the coming years.

However, I do feel that us academics need to do more here.  While I was comforted somewhat by what Kate had to say, I don’t think things are moving in the correct direction at all currently — as evidenced by the updated Oxfam report above.  On top of that, for every year these trends toward inequality continue, many many thousands more people across the world will die unnecessarily due to preventable causes, many of them attributable to inequality.  While it seems true that our community’s efforts to raise the profile of these issues are bearing fruit, we can’t hope to make a dent in these things by offering the occasional nugget of info every so often.  We should be taking sustained, concerted action.

That’s my view, anyway — and as anyone who knows me can testify, I’m always of the opinion that academics shouldn’t be afraid to step out of the ivory tower and cause a ruckus when we have something important to say.  In my mind, these studies of inequality are exactly that sort of thing — by pushing for action on these issues, which our community has studied a great deal, we can make a huge difference, even save many lives.  So we should follow up on this great work and keep up the pressure.

 

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York Workshop on Inequality

Yesterday I attended an event titled Have We Become Acclimatised to Greater Inequality?, an all-day workshop at the National Science Learning Centre at the University of York (programme).  The previous event in this same series focused primarily on health inequality — this event extended the scope of the discussion to take a look at inequality more generally, including economic and social inequality.

Policy Ignorance and the Low-Pay, No-Pay Cycle

The first session in the morning was split into two workshops — I attended the workshop run by Robert MacDonald, a fellow Teesside University academic.  Robert’s work focuses on youth unemployment and social exclusion in the Tees Valley area of the UK, an area frequently ranked amongst the most deprived in Britain.  As Robert pointed out, however, as recently as the 1970s the Tees Valley was one of the most economically vibrant parts of the country.  So what happened to cause this drastic decline in the area’s fortunes?

The government would have you believe that the deprivation and unemployment in the region is a consequence of a ‘culture of worklessness’ — a pathological lack of ambition, a disdain for hard work derived from families that supposedly lead a life of leisure, sitting around the house while claiming government benefits and refusing to work on gaining new skills to increase their employability.  Iain Duncan Smith, David Cameron, and others have made this argument, setting up an alleged conflict between ‘shirkers’ and ‘strivers’ — those who want ‘to get on’, versus those who prefer a life on benefits.

This is the government orthodoxy regarding unemployment, and has led to a policy programme which focuses on ‘up-skilling’ the workforce, increasing benefit conditionality (making it harder to claim benefits), and increasing the number of highly-skilled jobs while reducing the lower-skilled, lower-paid jobs.  Robert confidently called this ‘Voodoo Sociology’, and set out to explain why such a programme ignores the real reasons behind the deprivation and unemployment evident in areas like the Tees Valley.

Youth in the Tees Valley — Underambitious or Underemployed?

Robert and his colleagues have followed youth in the Tees Valley in a series of studies since 1998, called the Teesside Studies of Youth Transitions and Social Exclusion.  These studies found that, in contrast to the rhetoric of central government, the youth in the area have a constant engagement with the labour market — there is no such thing as a ‘culture of worklessness’.  Long-term, post-school transitions for Tees Valley youth are characterised by short-term, insecure jobs that are non-progressive — they don’t lead to further opportunities, promotion, etc.

So we do not see the kind of idle underclass proposed by the government, but instead a constant ‘churning’ of young people through the lowest end of the labour market.  Young people are continuously attempting to enter the labour market, only to be dumped after a few weeks or months and forced to claim Job-Seeker’s Allowance once again.  The DWP’s own studies confirm that of the 340,000 young people aged 22-24 who claimed JSA in 2010-11, 73% had claimed JSA at least once before.  Robert referred to this precarious labour market position as economic marginality — young people in the Tees Valley are perpetually stuck on the fringes of the labour market, with no clear path to regular employment or job security.

The Perils of Voodoo Sociology

Having set out these points, Robert returned to the government’s ‘Voodoo Sociology’.  The government policy goals around vastly increasing the supply of skilled workers, fuelled by a significant expansion of the higher education sector, has been done largely in isolation: there has been no corresponding increase in demand from employers for highly-skilled workers.  The trend we see of late is an increase in ‘lousy jobs’ — low-paid, low- or no-skilled, and insecure — and ‘lovely jobs’ — very highly-paid, highly skilled, and secure.  The middle ground has been ‘hollowed out’, leaving a significant percentage of university graduates with nowhere to go.  In areas like the Tees Valley this endemic underemployment is a serious issue, leaving some 34% of graduates in non-graduate-level jobs, even 5+ years after graduation.  Plus, thanks to recent government policy, these same graduates will soon be saddled with enormous educational debt as well.

Robert also spoke briefly about Prof Ken Roberts — a well-known academic in this area and author of several books on the topic, such as Youth in Transition: Eastern Europe and the West.  His work has confirmed across 25 countries that youth suffer no shortage of ambition, even in the most deprived areas.  In fact, youth repeatedly and doggedly attempt to engage in productive work, but the severe shortage of secure, progressive jobs for young people makes this a struggle.  Youth are seeking out the opportunities that are available to them — but the structure of these opportunities themselves are not conducive to getting young people out of poverty.

The Government’s Approach

Given all of this hard data, what response have we seen from the government?  Well, aside from a partial U-turn on tax credit cuts, an anaemic Living Wage policy, and some lip-service given to ‘making work pay’, not an awful lot.  We don’t see any concerted effort toward reducing the number of bad jobs out there, or restructuring the poor opportunities available to younger people.  Nor have we seen any support forthcoming for short-term underemployed people, or recurrently underemployed/unemployed youth.

Instead we have institutions like the Work Programme from the DWP, which with a success rate of 8% is actually worse than doing nothing at all (more than 8% of people find jobs by themselves, without taking assistance from the Work Programme).  Apprenticeship schemes only accept one of every 28 applicants, making them a very unlikely means of finding a new trade. Here in the Tees Valley, a new project costing £30 million (funded by the EU, as are many things around here — take note, UKIP) is aiming to address ‘social exclusion’ by making young people ‘more work ready’ and ‘raising their aspirations’.   So we see the exact same rhetoric — young people are to blame, their aspirations are too low, too many of them are long-term ‘NEET’ (not in employment, education or training).  When we look at the figures, less than 50 people in the entire region could be classed as actually long-term NEET — the overwhelming majority are constantly attempting to engage with a labour market that seemingly wants nothing to do with them.

So, having established that government policy on this issue is getting things disastrously wrong, and that young people are not in fact to blame for their own misfortune, why does the government persist in this approach?  Robert suggests that this ideology of the ‘undeserving unemployed’ provides an easy platform for the government to justify cuts to the welfare budget and sweeping austerity programmes.  Rolling out welfare-to-work programmes like the Work Programme is much easier than actually restructuring the labour market to create proper opportunities for youth — and large companies love these programmes, as they often end up getting free short-term labour out of it with no particular commitment to taking anyone on.  With that in mind Robert left us with a question at the end of his slides: as a society we speak often about young people’s aspirations and their supposed lack of same, but what about our aspirations?  Do we aspire to create a society in which our youth can find productive, secure employment, and if so, why aren’t we properly doing anything about it?

Summing Up

I very much enjoyed Robert’s presentation.  I found it revealing and very important — I just wish central government would give this kind of work the attention and respect it deserves.  I hope that I might be able to contribute to this kind of work sometime in the future, perhaps by developing simulations as testing grounds for testing the effects of relevant labour market reforms.

I was hoping to summarise the whole day in this post, but this has gone on long enough already — I’ll save the rest for another post.  I’ll spoil it for you now though and say I did enjoy the rest of the day as well.

Although, if I may offer some feedback for the organisers: as someone with a physical health problem which prohibits me from standing for long periods without extreme discomfort, please don’t hold lunch/networking sessions without any seating.  While everyone else was networking and chatting amiably, I ended up sitting in another room by myself, and that wasn’t overly pleasant.

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More Science about Science

After a good few hours working on the simulation yesterday — and by ‘a few’ I mean ’15 hours’ — I have things working in a more stable configuration now.  The original simulation I’m working from was structured around a stable population, but in this simulation I’m using a dynamic population — a very dynamic one, in fact, as postdocs shuffle in and out constantly.

This has meant that I’ve been working a lot on re-writing some of the code to facilitate the addition of postdocs to the virtual research community.  Yesterday I ended up learning some new skills when I found that I needed lists of agents that retained the order of the elements within, so that was an interesting opportunity to learn more about ordered dictionaries in Python.  Presumably I might be able to make use of those in future models too, so that’s very helpful.

So, at the moment we have a nicely dynamic population of simulated academic agents in which postdocs enter the population every semester as grants are disbursed to tenured academics.  Tenured academics spend their time doing research and applying for research grants; they learn from experience and change their time allocation strategies regularly to try to maximise their success in these arenas.  The simulation starts with 100 tenured academics, and after 50 years in a typical run we end up with about ~1200 academics in total, with about a third to a half of those being postdocs, depending on the parameter settings.

These results are based on a generous virtual society though, at least compared to reality: 25% of postdocs get promoted to tenured posts at the end of their contracts; research funding is available to about 30% of academics even as the population grows massively over the years; and tenured academics holding grants get a 50% boost to their research output.  Initially I had included a ‘management penalty’ to research quality for grant-holders, to account for the time spent line-managing postdocs and administering projects rather than actually doing research, but in this generous situation I left that penalty out completely.

So, in this relatively happy situation compared to the real world, do we see any productivity gain from the mass introduction of non-tenured, research-only staff?

Well… no, not quite:

r_mean_pdr

As you can see above, once postdocs are introduced we see a relatively precipitous drop in research productivity.  Grant-holders in particular suffer a great deal on this front, despite having that 50% research output bonus.  Tenured academics not holding grants (in purple) and failed grant applicants (yellow) also dip significantly, but then rebound slightly as they adjust their time allocation strategies between grant-writing and pure research.  Postdocs enter at a lower point and then settle at a middling level of productivity, necessitated by the lowered research productivity they experience at the beginning/end of their contracts.  Their output tends to be more ‘spikey’ in general, as they shuffle in and out of the population very frequently.  Toward the end of the simulation everyone begins to converge between the 0.3 – 0.5 range or so — and in this run we can see the postdocs just overtaking the grant-holders in productivity.

Another interesting aspect here is that in a no-postdoc situation there’s a reasonable positive correlation between research quality and grant disbursement — better researchers tend to get the money, in other words.  When postdocs are introduced that breaks down completely, and there’s little to no correlation between the two; in fact on more than a few runs I’ve seen slight *negative* correlations, this in spite of the fact that in the simulation research quality is used in the ranking of applications.

So — at this stage it seems like introducing a highly volatile, insecure population of researchers into the mix creates a large amount of uncertainty, reduces overall research output, and in general disrupts things significantly.  Even in a ‘generous’ research environment we see these problems clearly.

What about in a more challenging funding environment?  Let’s imagine we’re working in biology or something, one of those fields were grant applications only succeed 10-15% of the time, and money is scarce so permanent positions are even more difficult for postdocs to achieve:

r_mean_pdr

The population is much smaller, sustaining 605 academics in this particular run and just 96 postdocs — but the research output stats look extremely similar.  Grant-holders suffer a huge drop in overall productivity, punctuated by periods of high output when they’re holding that grant, and dipping again when they dump research time into grant-writing to try to get the next one.  Failed applicants and non-grant-holders still hover around the bottom edges, de-emphasizing research as they’re trying desperately to get research money through writing bids.  Postdocs, meanwhile, wobble around the 0.4 mark most of the time, never quite in post long enough to settle in  — and given that they’re not able to apply for grants, they never can benefit from that 50% bonus to output like the senior academics can.

Again these are early results and a very cursory analysis, but it seems like what’s happening here is pretty stable even with fairly significant changes to parameter settings (I’ve done many more runs on my own to check this).  This suggests that in order to escape these problems, future versions of the simulation will need to look at more drastic changes to the research career/funding structures in order to try to address these problems.

Next time, I’ll be adding some more analytical tools to the simulation, and developing some experiments to test alternative funding disbursement methods and career structures.  As ever please do get in touch with me if you have ideas or suggestions — I’m very keen to have more people to speak to about this kind of work!

 

 

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