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CX 2694, Pole Position!

Hello there, I hope that you all are safe and sane during this weird and difficult time. Today's episode is about Pole Position, the Namco arcade game that was ported to various Atari systems by General Computer Corp. (GCC if you're nasty). Coming up next is the wonderful Frostbite, by Activision's Steve Cartwright. If you have any thoughts on Frostbite (and I know that you do), please send them to me at 2600gamebygame@gmail.com by the end of the day on May 3rd. Thank you so much for listening and please take care of yourselves, I love you all!


Pole Position on KLOV
Pole Position on Random Terrain
Pole Position on Atari Protos
Pole Position on Atarimania
Betty Ryan Tylko profile on Atari Women
Atari Age thread about GCC credits
Atari Age thread, Undocumented Pole Position loaner cart
Atari Age thread - Pole Positn orange end label
1983 Atari Booth at CES video
Terry Hoff's web site
Marc Ericksen's web site
John Mattos' web site
Nerd Lunch Atari 2600 Retrospective
Batteries Not Included on Amazon
Imperial Scrolls of Honor Podcast
Nerd Noise Radio podcast

Toy Soldiers, Part 3: Core Space

When I was a kid Star Wars toys were all the rage. One thing that the developers of the original Star Wars action figures back in the 1970s got absolutely right was that the space ships, vehicles and especially the environments were at least as important as the characters, so they scaled their toy line in such a way that they could include play sets representing locations from the films for kids (and adults) to place the characters in.

Environment is at the heart of Core Space, a game developed by the makers of the Battle Systems line of cardboard miniatures terrain. Their sci-fi series, representing space station corridors, futuristic cities, and planetary outposts, was their most recent (and most successful) product to date, so naturally when they decided to create a game to go with their terrain, it was going to have a space opera theme.

While they could have gone with a standard skirmish game, they opted instead for something with a lot more narrative and adventure to it. Players control crews of scoundrels and pirates in what is essentially a sci-fi dungeon crawl, creeping around in labyrinthine space stations searching for cash and equipment (and yes, the loot is even stored in futuristic treasure chests). While players can opt to attack each others' crews, that would take precious time away from searching for goods and interacting with non-player civilians who might provide useful information or even be persuaded to join the crew.

If it sounds too easy, there's the inevitable catch: the galaxy the game takes place in is under attack from an enigmatic race of killer robots, and a few turns into the game those robots start appearing on the board, their actions controlled by a simple but very effective AI that governs where they move and who they attack. As the game progresses, more advanced (and deadly) models start appearing, and the tension ramps up. The game turns into a "press your luck" situation where you know you should be moving all your crew back to the airlock, but wait, there's a cargo crate we didn't look in yet...

The rules are simple and cinematic, using proprietary dice and an easy measuring system for combat, and there are lots of options for non-combat actions like interacting with civilians, opening and closing doors, using computers and even breaking and jumping through windows. And the fact that the game is designed specifically for the Battle Systems terrain means it gets the most out of it.

The game really shines when played in a series of linked missions over several games, using an experience system that allows crew members to improve their abilities and equipment over the course of the campaign. It even allows for improvements to the crew's space ship, allowing it to do things like scan the station for information or even defend the airlock from off-screen.

Rating: 5 (out of 5) a well designed game with rich, familiar-yet-different characters and a setting that can't help but be fully immersive thanks to the ingenious terrain.

• Core Space official website
• Core Space on BoardGameGeek

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Tech Book Face Off: Data Smart Vs. Python Machine Learning

After reading a few books on data science and a little bit about machine learning, I felt it was time to round out my studies in these subjects with a couple more books. I was hoping to get some more exposure to implementing different machine learning algorithms as well as diving deeper into how to effectively use the different Python tools for machine learning, and these two books seemed to fit the bill. The first book with the upside-down face, Data Smart: Using Data Science to Transform Data Into Insight by John W. Foreman, looked like it would fulfill the former goal and do it all in Excel, oddly enough. The second book with the right side-up face, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow by Sebastian Raschka and Vahid Mirjalili, promised to address the second goal. Let's see how these two books complement each other and move the reader toward a better understanding of machine learning.

VS.

Data Smart

I must admit; I was somewhat hesitant to get this book. I was worried that presenting everything in Excel would be a bit too simple to really learn much about data science, but I needn't have been concerned. This book was an excellent read for multiple reasons, not least of which is that Foreman is a highly entertaining writer. His witty quips about everything from middle school dances to Target predicting teen pregnancies were a great motivator to keep me reading along, and more than once I caught myself chuckling out loud at an unexpectedly absurd reference.

It was refreshing to read a book about data science that didn't take itself seriously and added a bit of levity to an otherwise dry (interesting, but dry) subject. Even though it was lighthearted, the book was not a joke. It had an intensity to the material that was surprising given the medium through which it was presented. Spreadsheets turned out to be a great way to show how these algorithms are built up, and you can look through the columns and rows to see how each step of each calculation is performed. Conditional formatting helps guide understanding by highlighting outliers and important contrasts in the rows of data. Excel may not be the best choice for crunching hundreds of thousands of entries in an industrial-scale model, but for learning how those models actually work, I'm convinced that it was a worthy choice.

The book starts out with a little introduction that describes what you got yourself into and justifies the choice of Excel for those of us that were a bit leery. The first chapter gives a quick tour of the important parts of Excel that are going to be used throughout the book—a skim-worthy chapter. The first real chapter jumps into explaining how to build up a k-means cluster model for the highly critical task of grouping people on a middle school dance floor. Like most of the rest of the chapters, this one starts out easy, but ramps up the difficulty so that by the end we're clustering subscribers for email marketing with a dozen or so dimensions to the data.

Chapter 3 switches gears from an unsupervised to a supervised learning model with naïve Bayes for classifying tweets about Mandrill the product vs. the animal vs. the Mega Man X character. Here we can see how irreverent, but on-point Foreman is with his explanations:

Because naïve Bayes is often called "idiot's Bayes." As you'll see, you get to make lots of sloppy, idiotic assumptions about your data, and it still works! It's like the splatter-paint of AI models, and because it's so simple and easy to implement (it can be done in 50 lines of code), companies use it all the time for simple classification jobs.

Every chapter is like this and better. You never know what Foreman's going to say next, but you quickly expect it to be entertaining. Case in point, the next chapter is on optimization modeling using an example of, what else, commercial-scale orange juice mixing. It's just wild; you can't make this stuff up. Well, Foreman can make it up, it seems. The examples weren't just whimsical and funny, they were solid examples that built up throughout the chapter to show multiple levels of complexity for each model. I was constantly impressed with the instructional value of these examples, and how working through them really helped in understanding what to look for to improve the model and how to make it work.

After optimization came another dive into cluster analysis, but this time using network graphs to analyze wholesale wine purchasing data. This model was new to me, and a fascinating way to use graphs to figure out closely related nodes. The next chapter moved on to regression, both linear and non-linear varieties, and this happens to be the Target-pregnancy example. It was super interesting to see how to conform the purchasing data to a linear model and then run the regression on it to analyze the data. Foreman also had some good advice tucked away in this chapter on data vs. models:

You get more bang for your buck spending your time on selecting good data and features than models. For example, in the problem I outlined in this chapter, you'd be better served testing out possible new features like "customer ceased to buy lunch meat for fear of listeriosis" and making sure your training data was perfect than you would be testing out a neural net on your old training data.

Why? Because the phrase "garbage in, garbage out" has never been more applicable to any field than AI. No AI model is a miracle worker; it can't take terrible data and magically know how to use that data. So do your AI model a favor and give it the best and most creative features you can find.
As I've learned in the other data science books, so much of data analysis is about cleaning and munging the data. Running the model(s) doesn't take much time at all.

We're into chapter 7 now with ensemble models. This technique takes a bunch of simple, crappy models and improves their performance by putting them to a vote. The same pregnancy data was used from the last chapter, but with this different modeling approach, it's a new example. The next chapter introduces forecasting models by attempting to forecast sales for a new business in sword-smithing. This example was exceptionally good at showing the build-up from a simple exponential smoothing model to a trend-corrected model and then to a seasonally-corrected cyclic model all for forecasting sword sales.

The next chapter was on detecting outliers. In this case, the outliers were exceptionally good or exceptionally bad call center employees even though the bad employees didn't fall below any individual firing thresholds on their performance ratings. It was another excellent example to cap off a whole series of very well thought out and well executed examples. There was one more chapter on how to do some of these models in R, but I skipped it. I'm not interested in R, since I would just use Python, and this chapter seemed out of place with all the spreadsheet work in the rest of the book.

What else can I say? This book was awesome. Every example of every model was deep, involved, and appropriate for learning the ins and outs of that particular model. The writing was funny and engaging, and it was clear that Foreman put a ton of thought and energy into this book. I highly recommend it to anyone wanting to learn the inner workings of some of the standard data science models.

Python Machine Learning

This is a fairly long book, certainly longer than most books I've read recently, and a pretty thorough and detailed introduction to machine learning with Python. It's a melding of a couple other good books I've read, containing quite a few machine learning algorithms that are built up from scratch in Python a la Data Science from Scratch, and showing how to use the same algorithms with scikit-learn and TensorFlow a la the Python Data Science Handbook. The text is methodical and deliberate, describing each algorithm clearly and carefully, and giving precise explanations for how each algorithm is designed and what their trade-offs and shortcomings are.

As long as you're comfortable with linear algebraic notation, this book is a straightforward read. It's not exactly easy, but it never takes off into the stratosphere with the difficulty level. The authors also assume you already know Python, so they don't waste any time on the language, instead packing the book completely full of machine learning stuff. The shorter first chapter still does the introductory tour of what machine learning is and how to install the correct Python environment and libraries that will be used in the rest of the book. The next chapter kicks us off with our first algorithm, showing how to implement a perceptron classifier as a mathematical model, as Python code, and then using scikit-learn. This basic sequence is followed for most of the algorithms in the book, and it works well to smooth out the reader's understanding of each one. Model performance characteristics, training insights, and decisions about when to use the model are highlighted throughout the chapter.

Chapter 3 delves deeper into perceptrons by looking at different decision functions that can be used for the output of the perceptron model, and how they could be used for more things beyond just labeling each input with a specific class as described here:

In fact, there are many applications where we are not only interested in the predicted class labels, but where the estimation of the class-membership probability is particularly useful (the output of the sigmoid function prior to applying the threshold function). Logistic regression is used in weather forecasting, for example, not only to predict if it will rain on a particular day but also to report the chance of rain. Similarly, logistic regression can be used to predict the chance that a patient has a particular disease given certain symptoms, which is why logistic regression enjoys great popularity in the field of medicine.

The sigmoid function is a fundamental tool in machine learning, and it comes up again and again in the book. Midway through the chapter, they introduce three new algorithms: support vector machines (SVM), decision trees, and K-nearest neighbors. This is the first chapter where we see an odd organization of topics. It seems like the first part of the chapter really belonged with chapter 2, but including it here instead probably balanced chapter length better. Chapter length was quite even throughout the book, and there were several cases like this where topics were spliced and diced between chapters. It didn't hurt the flow much on a complete read-through, but it would likely make going back and finding things more difficult.

The next chapter switches gears and looks at how to generate good training sets with data preprocessing, and how to train a model effectively without overfitting using regularization. Regularization is a way to systematically penalize the model for assigning large weights that would lead to memorizing the training data during training. Another way to avoid overfitting is to use ensemble learning with a model like random forests, which are introduced in this chapter as well. The following chapter looks at how to do dimensionality reduction, both unsupervised with principal component analysis (PCA) and supervised with linear discriminant analysis (LDA).

Chapter 6 comes back to how to train your dragon…I mean model…by tuning the hyperparameters of the model. The hyperparameters are just the settings of the model, like what its decision function is or how fast its learning rate is. It's important during this tuning that you don't pick hyperparameters that are just best at identifying the test set, as the authors explain:

A better way of using the holdout method for model selection is to separate the data into three parts: a training set, a validation set, and a test set. The training set is used to fit the different models, and the performance on the validation set is then used for the model selection. The advantage of having a test set that the model hasn't seen before during the training and model selection steps is that we can obtain a less biased estimate of its ability to generalize to new data.

It seems odd that a separate test set isn't enough, but it's true. Training a machine isn't as simple as it looks. Anyway, the next chapter circles back to ensemble learning with a more detailed look at bagging and boosting. (Machine learning has such creative names for things, doesn't it?) I'll leave the explanations to the book and get on with the review, so the next chapter works through an extended example application to do sentiment analysis of IMDb movie reviews. It's kind of a neat trick, and it uses everything we've learned so far together in one model instead of piecemeal with little stub examples. Chapter 9 continues the example with a little web application for submitting new reviews to the model we trained in the previous chapter. The trained model will predict whether the submitted review is positive or negative. This chapter felt a bit out of place, but it was fine for showing how to use a model in a (semi-)real application.

Chapter 10 covers regression analysis in more depth with single and multiple linear and nonlinear regression. Some of this stuff has been seen in previous chapters, and indeed, the cross-referencing starts to get a bit annoying at this point. Every single time a topic comes up that's covered somewhere else, it gets a reference with the full section name attached. I'm not sure how I feel about this in general. It's nice to be reminded of things that you've read about hundreds of pages back and I've read books that are more confusing for not having done enough of this linking, but it does get tedious when the immediately preceding sections are referenced repeatedly. The next chapter is similar with a deeper look at unsupervised clustering algorithms. The new k-means algorithm is introduced, but it's compared against algorithms covered in chapter 3. This chapter also covers how we can decide if the number of clusters chosen is appropriate for the data, something that's not so easy for high-dimensional data.

Now that we're two-thirds of the way through the book, we come to the elephant in the machine learning room, the multilayer artificial neural network. These networks are built up from perceptrons with various activation functions:

However, logistic activation functions can be problematic if we have highly negative input since the output of the sigmoid function would be close to zero in this case. If the sigmoid function returns output that are close to zero, the neural network would learn very slowly and it becomes more likely that it gets trapped in the local minima during training. This is why people often prefer a hyperbolic tangent as an activation function in hidden layers.

And they're trained with various types of back-propagation. Chapter 12 shows how to implement neural networks from scratch, and chapter 13 shows how to do it with TensorFlow, where the network can end up running on the graphics card supercomputer inside your PC. Since TensorFlow is a complex beast, chapter 14 gets into the nitty gritty details of what all the pieces of code do for implementation of the handwritten digit identifier we saw in the last chapter. This is all very cool stuff, and after learning a bit about how to do the CUDA programming that's behind this library with CUDA by Example, I have a decent appreciation for what Google has done with making it as flexible, performant, and user-friendly as they can. It's not simple by any means, but it's as complex as it needs to be. Probably.

The last two chapters look at two more types of neural networks: the deep convolutional neural network (CNN) and the recurrent neural network (RNN). The CNN does the same hand-written digit classification as before, but of course does it better. The RNN is a network that's used for sequential and time-series data, and in this case, it was used in two examples. The first example was another implementation of the sentiment analyzer for IMDb movie reviews, and it ended up performing similarly to the regression classifier that we used back in chapter 8. The second example was for how to train an RNN with Shakespeare's Hamlet to generate similar text. It sounds cool, but frankly, it was pretty disappointing for the last example of the most complicated network in a machine learning book. It generated mostly garbage and was just a let-down at the end of the book.

Even though this book had a few issues, like tedious code duplication and explanations in places, the annoying cross-referencing, and the out-of-place chapter 9, it was a solid book on machine learning. I got a ton out of going through the implementations of each of the machine learning algorithms, and wherever the topics started to stray into more in-depth material, the authors provided references to the papers and textbooks that contained the necessary details. Python Machine Learning is a solid introductory text on the fundamental machine learning algorithms, both in how they work mathematically how they're implemented in Python, and how to use them with scikit-learn and TensorFlow.


Of these two books, Data Smart is a definite-read if you're at all interested in data science. It does a great job of showing how the basic data analysis algorithms work using the surprisingly effect method of laying out all of the calculations in spreadsheets, and doing it with good humor. Python Machine Learning is also worth a look if you want to delve into machine learning models, see how they would be implemented in Python, and learn how to use those same models effectively with scikit-learn and TensorFlow. It may not be the best book on the topic, but it's a solid entry and covers quite a lot of material thoroughly. I was happy with how it rounded out my knowledge of machine learning.

Missed Classic: Moonmist - Representation Blues

Written by Joe Pranevich

I intended to wrap up Moonmist this week by closing out on the three remaining cases then moving quickly to the final rating. I did not make it. When playing and reviewing, I try to come to these games as unspoiled as I can. I learn what I need to discuss the history and place the game in context, but I avoid spoiling the plots and puzzles as much as I can. Usually that works, but in this case I missed one of the things that Moonmist is most remembered for: it is (supposedly) the first computer game to feature LGBT characters. I disagree with that assessment, but we'll get there soon enough. It seems poor form for me to review this game, in Pride Month of all times, without giving space to discuss this important aspect of gaming history.

This week, I'm looking at the "blue" variant of Moonmist, the second one listed in the manual. (I finished "red" last week.) To the best of my knowledge, this is the only version that includes a LGBT-related plotline, but I have not played the others yet. I will take a quick look at LGBT representation in media more broadly into the 1980s and then dive into whether or not this game deserves its spot as the "first". Of course, I'll also be playing and solving the mystery itself! I hope that the final two variations don't have more surprises that lead to hours of research and introspection. Read on for more.

Boston Pride in the mid-1980s.

I am not an expert on LGBT issues in pop culture and I encourage our commenters to tell me all of the details that I am sure to be missing. As a child of the 1980s, I grew up steeped in the stereotypes that pervaded America when this game was written. Jim Lawrence and Stu Galley are older still and grew up with the stereotypes that they picked up from the media of their day. Those attitudes stemmed from even earlier depictions in books and film. Attitudes are far from unchanging, but each successive generation carries a bit of the baggage of the previous.

At least in the United States, one of the ways in which pop culture shaped attitudes towards homosexuality is through the "Hays Code", or more properly the "Motion Picture Production Code". That is not to say that discrimination didn't exist before-- that code itself was a product of generational attitudes-- but it codified (for film) a set of rules that was followed from the 1930s through the 1960s and persisted even later through the threat of boycotts and self-censorship. Similar codes existed in other media, but it is undeniable that the Hays Code helped to reinforce the way "average" Americans felt about certain issues. This is not limited to homosexuality! These rules banned depictions of inter-racial relationships, criticism of religion, pre-marital sex, and many other things. You could not portray a criminal as sympathetic. You had to show respect for law enforcement. Homosexuality, considered a "sexual perversion", could be depicted only as a trait of a villain. LGBT characters in these films were murders and sadists brought to justice, or emotionally challenged individuals prone to suicide. Gay character traits became associated with villany. Long after the Hays Code fell out of favor, these tropes remained in use, burned into society's collective unconscious.

It was Mr. Green with the (suggestive) pipe!

To take one small example, I looked last week at the film Clue and how it may have inspired Moonmist by featuring multiple endings. In that film, Mr. Green is depicted as a gay man who lives in constant fear of being discovered and losing his job at the State Department. This makes him an easy target for extortion. He's a bumbling fool, although perhaps not much more than others in this comedy-mystery. It is only in the "real" ending of the film that Mr. Green is revealed to be a hero: he's an undercover FBI agent who was working to expose the crimes of Mr. Boddy and the rest of the houseguests, all of whom had murdered someone over the course of the night. (Communism was a "red herring"!) But in that crowning moment of awesomeness, Mr. Green's gayness was stripped away. As the police arrest the guests, Green speaks the final line of the film: "OK Chief, take them away! I'm gonna go home and sleep with my wife!" Even forty years later, LGBT representation in media often falls into established patterns. Gay characters are still often driven to suicide. If they don't do it to themselves, they could be killed by something else, and may be the first in line to be killed in such a way.

I will spoil the ending a bit to say that this Moonmist variant falls right back on these tropes. In the "blue" mystery, Deirdre and Vivien are revealed to have been lovers. Deirdre is bisexual and torn between her love for a man (Lord Jack) and Vivien. Ultimately, she surrenders to suicide by jumping in the well in the basement of the castle. In comic book fashion, Vivien swears revenge on the man that took her love away. It is perhaps progressive by including gay characters at all, but these are the same "murderer" and "suicide" options that were all the rage during the Hays Code days.

I love a good Mac adventure.

After all that, was Moonmist really the first video game to include LGBT characters? Unfortunately not. We already saw one example in this very series! Leather Goddesses of Phobos includes a scene where the player character can choose to have sex with one of the titular Goddesses just before the end of the game. Unique across all of the sexual interactions you can have, this is the only one that is not gendered: the Goddesses are female whether you play as a man or a woman. If you choose to go that route, you can prove that your player character is not only bi-curious, but the villains are bisexual as well. They are still murderers and sadists, of course, fitting the evil gay trope exactly, but the game did come out a few months prior to Moonmist.

Another set of examples come from Europe, admittedly in games that most Americans would not have played. Two games by Froggy Software, written in French, feature gay villains:

• Le crime du parking (1985) - In "The Parking Lot Crime", the villain is a gay drug dealer.
• Le mur de Berlin va sauter (1985) - In "The Berlin Wall Will Blow Up", the villain is a gay terrorist who wants to destroy the Berlin Wall. Maybe he wasn't all bad?

Not a single one of these games includes a positive depiction of LGBT characters!

To find a positive depiction, we have to turn the clock forward to 1989 and the graphical adventure Caper in the Castro by C. M. Ralph. Following indirectly in the footsteps of ICOM games such as Déjà Vu (1985), it features an on-the-nose detective named Tracker McDyke as she investigates the disappearance of a drag queen friend. She will have to search San Francisco's famous Castro District, one of the most famous and earliest gay neighbothoods in the country, to solve the case. Ralph released the game to help raise money for AIDS-related charities; she also produced a "straight" variant (Murder on Mainstreet) that she expected would garner wider distribution. Once believed lost, this game has been recovered thanks to the efforts of the Internet Archive and Strong Museum of Play. I have added it to my personal "to play" list and hope to dive into it at some point in the future.

Where were we? Oh right, this is supposed to be a post about Moonmist.

I'm blue da ba dee da ba daa.

Playing the Blues

After finishing the first variant last week, I have no idea how the rest of them will go. Will they be mostly the same? Mostly different? How bored will I get if I must explore everything again? For this version, I'm choosing a female character again ("Ms. Blueish"), but I'll use a male one and a "not specified" one for the final two just to see what is different. I will not recap everything that happens; please check out the previous two posts if you need a refresher.

After starting the game and picking my color, the game proceeds as before. The first change comes when I ask Tamara about the ghost. She tells me that she saw the ghost again, this time outside the office brandishing a sword. Tamaram fled back inside and locked the door. After a time, the ghost disappeared and was not seen again.

Just as before, Tamara leads me from room to room to introduce me to the guests. Most of the responses are unmemorable, but Vivien's stand out. Let me quote them in full:

> ask vivien about ghost

She is somber as she replies, "I dare say it was morbid of me but one night I went to the basement, just to try to imagine the horrible scene where poor Deirdre suffered her… tragic accident. Suddenly I heard somebody calling my name softly. I turned 'round and there was Deirdre herself standing by the stairs! I went absolutely numb! She smiled faintly, then fled up the stairs. I started to follow, but then I knew it was no use. Deirdre is dead and gone, and chasing her ghost won't bring her back to me.

> ask vivien about Deirdre

The artist shrugs with a sad, wistful smile. "What can I say? Deirdre was a most unusual girl… utterly unworldly… almost fey. She grew up in a cottage not far from here, you know. Her drowning was a terrible tragedy… and yet… sometimes I'm not sure she WANTED to go on living." She turns her face away to hide a tear.

"Chasing her ghost won't bring her back to me." Does that sound like I think it sounds? Were Vivien and Deirdre together!? That would be an amazing twist, if so. We know that Deirdre was engaged to Jack in all of the versions since it is in the manual text. Was she only with Jack to hide a secret love affair? Or was this something simpler, like a childhood dalliance? And how could a socialite like Deirdre even meet a poor woman that grew up in a nearby cottage? Perhaps her art inspired her to fraternize with the locals? It seems like they would have had quite different social circles.

Something like this?

We are eventually brought to our room to freshen up. Just as before, Bolitho, the butler, stops by for a chat. He also spied the White Lady in this version, except now she was in the New Great Hall and searching on the floor like she needed glasses! The butler also seems to be hinting about how to open the secret passage in my room. The language is exactly the same as before, but I suspect that I just didn't catch on until I learned more about how the passages work. A nice little detail!

I dress and head downstairs for dinner. I get there a few minutes early so I have time to search the New Great Hall on the way. Somehow managing to remain untrampled, I discover a contact lens on the floor. The ghost really did need glasses! Who could it belong to?

The dinner party proceeds as before with the butler leaving a note about the staff leaving, Jack announcing his engagement, and Lionel's recorded voice from beyond the grave surprising his guests with a "scavenger hunt". The first clue is still hidden under the punch bowl, but this time it is a picture of a skeleton in a Chinese Mandarin costume. What could that mean? The second clue is given to Jack this time and it is a rhyming poem with some words missing: 

Three fellows argued about life:

1. 'Using this motto, no chap can go wrong:

Leave the wench and the grape, and go with a _____!

2. 'On the seas of my life is a ship that is laden

Not with bottles or tunes, but with innocent ____s!

3. 'Women and singing are both very fine,

But for me there is nothing to equal good _____!

The answers are simple, especially since the topics are reiterated in all three stanzas: "song", "maiden", and "wine". Thanks to my exploration last time, I know there is a wine cellar in the basement, an iron maiden in the dungeon, and a piano in the sitting room. Plaything through multiple times has advantages!

Since everyone is together, I ask about glasses and the lost contact lens. Would anyone be dumb enough to admit it? Dr. Wendish wears glasses but says that he cannot stand contacts. Hyde wears a monocle. Vivien claims that she cannot tolerate contacts but wears glasses for close-up work in her art. No immediate clues there.

The party moves to the sitting room. I grab the maid's note off the desk and it's the same as before but ends with a strange warning:

Me Dad always says that the first sign of a nut case is when a person starts talking to hisself. Well, if you was to ask me, there is more than ne way to talk to himself. Some does it on paper, and that is the type person to watch out for.

I still hate the fake accented speech. I also have no idea what this means, except that I should be on the lookout for a villain that leaves Post-Its around the mansion documenting his or her crimes.

Armor or Armour? You decide.

Since I am here, I check the piano. Instead of music from A Prairie Home Companion, the piano now has Beethoven's Suite #9 ready to be played. Someone has circled the "SUIT" in the title. That must be a clue! I immediately check the suit of armor in the hall and am rewarded with yet another clue. This is going very quickly! Unfortunately, it isn't quite as self-explanatory as the others:

My al___ has no glamour;

Its '___e' tones do clam___.

Can you find me?

I have no idea what that means so I head down to the wine cellar instead. As expected, I locate a bottle of wine with "OUR" circled on the label. I get cocky and guess that the iron maiden will have an "ARM" label on it someplace, but I am disappointed. Two out of three isn't bad! I'm certain that the clue is just telling me to search the armor, so it is no longer necessary. While exploring, I notice that this time it is Vivien and not Jack who is scouring the house for treasure. Jack is content to let someone else find his family's priceless heirloom? I still do not understand the rules of this scavenger hunt.

It takes only a few minutes of searching to discover a fossil skull hidden in the bell on the roof of the castle. My hint was that the word "clamour" would have rhymed with "glamour" and that was the only clamorous object I could remember. I have no idea what the other blanks are supposed to mean, but it hardly seems to matter now.

A fancy contact lens case from the 1980s.

It's only 9:45 PM! I am making excellent time through the game, but I still need to figure out who the ghost is. With Vivien busy searching the castle, I sneak into her room and search. Inside her art supply box, I discover a contact lens case with a missing contact. Score! Vivien is the ghost, but why? I grab the box and show it to her, but she claims that I planted it in her room to frame her. I try a more direct approach by hiding in the secret passages until the ghost appears. That worked last time and it works again! This time, the White Lady appears armed with a blowgun. I quickly fire the butler's aerosol can at her and she falls to the floor unconscious. I search her to confirm that yes, it is Vivien. Worse, her blowgun contained a real poisoned dart. She was out to kill someone tonight… but who?

I wake Vivien but instead of admitting it all, she sort of sleepwalks to her room. What was in that spray? Once there, she still doesn't admit anything. I show her the ghost costume and she accuses me of planting it! I just caught you in the secret passage! The nerve of some people.

The step that I missed ends up being simple: if I had looked in Vivien's art supply box again after removing the contact lens case, I would have discovered her diary. Reading that reveals a tear-stained page:

O Deirdre, sweet Deirdre! Jack will pay dearly for your cruel death by losing his new sweetheart...

That gives us our motive and we can finally accuse her of being the ghost. Bolitho appears and takes her away. The narrator reveals what really happened:

Vivien was intensely attached to Deirdre, and she jealously hated Lord Jack for coming between them. When Deirdre accidentally fell down the well, Vivien was convinced that she had committed suicide because she felt abandoned by Jack.

So Vivien began her vengeful ghostly masquerade -- to find proof that Jack was responsible for Deirdre's death, to prick his guilty conscience and make him confess, and to terrorize Tamara, who replaced Deirdre in Jack's affections.

This time around, Vivien didn't actually kill anyone. Deirdre's death was an accident rather than a suicide-- although I'm not sure I believe that-- and Vivien wanted revenge on Jack for it. It's all rather complicated. It also means that the maid must have read her diary which also just comes off as creepy, although not as creepy as dressing in a glow-in-the-dark ghost costume. (Yes, you can use it as a light source!) In this version, either Lionel's death was natural or Jack was much better about hiding it.

It actually doesn't seem impossible that, other than the ghost, the stories aren't mutually exclusive. Jack could still have killed Lionel and Deirdre, just as in the "red" version, but this time Deirdre is either really dead or has no interest in coming back to either of her two lovers. Will the rest of the cases fit together as well? We'll have to play them to see.

With luck, next week will really be the Final Rating. Thanks for humoring me through this special look at the "blue" version. Happy Pride!

Time Played: 1 hr 20 min
Total Time: 6 hr 45 min

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Why Self-Compassion?

I've read so many self-help books, and upon reviewing the most helpful ones, I keep saying, "that's another way of saying to have self-compassion". The concept that ties all the ideas in these self-help books boils down to self-compassion.

I also recommend The 7 Habits of Highly Effective People for a roadmap on how to organize your life in a practical manner, as a very effective and powerful way of doing your to-do lists, and so forth. I summarized the book here in two parts, this is the first part.

But it's really self-compassion that can motivate you to be effective in the first place, and to really stick to your goals! I wasn't able to follow any sort of positive habits for long when I read The 7 Habits in college, because of being easily demoralized.

Therefore, in this post, we'll explain why self-compassion is such a powerful concept. Practicing self-compassion is personal and isn't applied in a "cookie-cutter" way. In fact, having self-compassion is extremely challenging and difficult, as you have to find out what works for you.

In this past post, I superficially touched upon a self-compassion exercise, so in this post, we'll explain why self-compassion is key, by summarizing Self-Compassion: The Proven Power of Being Kind to Yourself  by Kristen Neff, Ph.D., who is the foremost authority on the subject.

In the horrible Harry Harlow experiments, he nevertheless proved that love and connection are more basic than food and water. The poor baby monkeys were taken away from their mothers, and had to choose between the fake cloth mom with no food/water, and the fake wire mom with food/water.

Harlow himself thought that the babies would stick with the wire mom the whole time because of the food and water, but found out the exact opposite. The babies clung to the cloth mom and when hungry, run toward the food/water, and then immediately run back to the cloth mom.

What this experiment proved is that the basic need of all humans is love and connection, more so than even food and water. When you don't have love and connection with others, it can lead to depression, anxiety and even suicide.

This sense of belonging is primary and deep, even in the most "macho" rituals, American football. By being a diehard fan of one team, you embrace other fellow fans. You see strangers hugging each other, sharing food and beer in these "tail-gate" parties. There's a huge sense of connection if you ever participated in one of these parties.

Having self-compassion sounds soft and fluffy, but in reality, it can be very tough and painful at times, as we will see down the road.

PROBLEM ONE: Comparing ourselves to others leads to disconnection and suffering

At least in the Western world, we live in an extremely competitive society where we must excel, and it's not "good enough" to be average, you have to be above average.

This is so illogical, because we can't be above average in all things, and there are always going to be people who are more beautiful, smarter, more successful than us.

Sadly, by comparing ourselves to others and being competitive in wanting to be above-average, people tend to look down upon others to feel better about themselves.

We may get a rush from having higher self-esteem when we mistakenly feel that we are more "successful" than another person.

However, if we meet someone who is more "successful" than us, then our self-esteem plummets, and we feel like crap.

Therefore, comparing ourselves to others leads to this emotional see-saw. If we find we're better, we get elated, if we don't measure up, we get depressed.  Even worse, when we protect our self-image to avoid feeling bad about ourselves, we don't acknowledge our faults, rather blaming the other person, even though "it takes two to tango".

This leads to ongoing conflicts, which causes disconnection from your loved ones. Further, by not seeing our flaws, that leads to stagnation and lack of growth, because how can you improve if you don't acknowledge your faults?

The solution to prevent these comparisons is self-compassion. Stop judging and evaluating ourselves altogether! Don't label ourselves as "good" or "bad" but rather accept ourselves openly, and treat ourselves with kindness like a best friend.

Does this work? Yes! By having self-compassion you accept yourself because you're like everybody else! Everyone has flaws, we're no different. By accepting ourselves for who we are, then we can accept others as well, and there's no reason to compare.

When you love and truly accept yourself, you're not going to look down on those who are less fortunate. Likewise, you're not going to have that sinking feeling that you're not doing enough when you see others drive fancy cars.

Caveat: There are many people who are rather harsh with themselves, but would never be that way with others. However, by being nasty to yourself, you're not going to feel good about yourself.

Why not pursue Win/Win where you're compassionate towards yourself and others?

I notice that I tend to feel sour when someone I dislike becomes more successful (comparing), and I get down on myself for not being that successful. Then I feel bad that I can't forgive the person and let go. It's an absolutely awful feeling, it doesn't do me any good, and certainly not to the more successful person. I really hate that pinched soul feeling.

Next, I continue to feel bad about myself for not being charitable, and this spirals downward to being angry with myself, "why can't I just forgive!".

However, when I have self-compassion and realize that forgiveness is something I struggle greatly with, and indeed a lot of people have the same issues, I can be more patient with myself and move toward being less judgmental.

Allowing yourself to be kind to yourself humanizes you (as you suffer just like everyone), as well as humanizing others because you understand deep down that they're going through same and/or different struggles as well.

In other words, as part of humanity, you are a worthy person, just like everyone else. When you see yourself as different than others, that again leads to feeling disconnected and not belonging to humanity.

Indeed, dehumanizing others leads to disconnection, which has led to unspeakable crimes against humanity. By seeing "non-Aryan" groups as other and less than human, it was easy for an entire nation to exterminate and torture people "because they're not like us".

PROBLEM 2: Feeling lonely and isolated

We looked at the first part of self-compassion which is self-kindness: gentle understanding of ourselves, rather than being critical and judgmental.

The second part of compassion we briefly touched upon. Why should we be kind to ourselves? Because we're all part of humanity. As we're kind to others, then it makes logical sense to be kind to ourselves.

The concept here is "we're all in this together". We recognize this common human experience of suffering, acknowledging the interconnected nature of our lives (Harry Harlow experiment), indeed life itself.

Therefore, compassion is relational. By seeing people as part of humanity, rather than "other" as the Nazis did, we feel connected.

As explained above, our deepest need is to belong, but when you compare yourself to others, this disconnect leads to loneliness. The KKK feel superior to others because they're white, and the "other" is not. The same can be said of Men, Women, Democrats, Republicans, Americans, Russians, Christians, Muslims, and the list goes on. We're part of this group, therefore, we're superior to this other group. Fanatical group identity is dangerous as it leads to disconnection and even genocide. 

However, if you refuse to hold this view and have compassion toward yourself and others, regardless of group affiliation, you have connection. Instead of seeing differences, you reframe and see how we're so similar to one another. We all want love and connection; that's our similarity.

So when our sense of self-worth and belonging is grounded in simply being human, we can't be rejected or cast out by others. It makes no sense to say that you're rejected by humanity, because you're human.

Remember your shared humanity. That can help you to have compassion for who you are. It helps to have others be kind toward you, but they can't be there with you 24/7. However, you can be with yourself 24/7, so you might as well be kind to yourself using the "best friend" approach discussed in this post.

PROBLEM 3: Suffering

This is the hard part of self-compassion. Self-kindness and common humanity we discussed above. The third and last step is mindfulness.

You must be aware of your suffering, but in a balanced way, where you neither diminish, or make it out to be worse than it is. I tend to make a mountain out of an ant-hill.

Therefore, in this third part of self-compassion, you need to be mindful - clear seeing and nonjudgmental acceptance of what's occurring in the present moment.

You're facing up to reality, neither underestimating or over-exaggerating things. First step is to recognize when you're suffering instead of suppressing it, because you can't heal what you can't feel. Be aware of your pain. By stuffing and ignoring pain, it can explode.

A good analogy of awareness is thus: Awareness is the blue sky. Your feelings and thoughts are the birds flapping around. Identify with the sky, instead of the birds. If you remain in awareness (i.e. sky) and not react to the thoughts and feelings (birds), you can be calm and centered as the sky doesn't shift and change in a feckless manner.

You can't change your thoughts and feelings very well, but you can change your reactions to them. There are many meditation techniques, but the key here is to hold and be aware of the pain, and don't numb it.

Indeed, people who suffer from PTSD tend to numb their emotions, as a very understandable mechanism to avoid feeling the immense pain of trauma.

But by having this numbing of emotions, they can't feel the positive emotions of joy, creativity, love. When you numb, you numb all emotions. Often, people who suffer from PTSD say that they're living zombies and they don't know how to have fun anymore.

The hard work in PTSD involves working through the painful memories in a safe, secure environment. The acknowledgment, and being one with the pain, is the really difficult part of self-compassion.

One example that makes us all feel bad about ourselves is when we hear a baby crying which irritates us, but we judge ourselves for having these thoughts, "what a horrible person I am for having that thought, it's only a baby, a nicer person would feel sympathy rather than being triggered".

However, if you have self-compassion, you stop the judgment. You become aware (sky) of the irritation (birds) you're having, you acknowledge the negative thought, while recognizing that surely a lot of people would feel the same way, and the thought will eventually pass!

A silly example is when I went to a party. I tend to need at least 5 large glasses of wine and/or beer to feel socially comfortable. The extremely uncomfortable feeling of being socially awkward has been too hard for me to deal with.

However, at a recent party, I decided not to drink - this wasn't too daring, because there were only 3 people at the party that I don't know that well. I decided to practice self-compassion, since I just completed reading the book.I decided to be one with being socially awkward.

What helped me was chanting exactly how I felt, "socially awkward, socially awkward, socially awkward". However, after 1 hour (I'm "slow to warm up"), I stopped feeling awkward, and I ended up enjoying being in the moment and having meaningful connections.

I'm not sure if this strategy would work if I'm in a party with people I barely know, but this is a small step to being aware.

Dr. Neff recommends that when you feel suffering, to have a mantra, in your words, along this line:

This is a moment of suffering
Suffering is part of life
May I be kind to myself in this moment
May I give myself the compassion I need

I kind of like Brene Brown, Ph.D. (author of Daring Greatly) mantra where one of her interviewees, when in pain would simply say, "pain, pain pain". Or you can say "ouch, ouch, ouch", to acknowledge the pain, as well as the rest of the mantra as suffering being part of humanity, and to give yourself kindness and compassion. It's best if it's in your own words.

On a positive note, when you have awareness, you're going to have awareness of positive emotions too! In this situation, you can hold it in loving awareness and really make that feeling bloom! You can experience love and joy with more awareness and rejoice in it - it actually overflowed to my coworkers and strangers!

Using the three part component of self-compassion as a way toward love and connection, it helps you to deal with pain and suffering.

I then chuckled at Dr. Neff's stages of self-compassion, because I went through the same thing. Initially, as I had self-compassion, I had this outpouring of love toward my coworkers, and work was light - I actually made some rather creative suggestions which surprised even me. I was enamored with self-compassion.

However, I then saw the hard work of self-compassion. It doesn't take the pain away at all, rather it helps you to be more resilient and deal with pain in a more effective way.

Instead of numbing or burying your feelings, which will pop up again, as survivors of trauma would all attest, in the form of disturbing intrusions, horrific nightmares and flashbacks, rather self-compassion holds you in awareness.

With self-compassion, you gain the resilience to work on painful emotions, feelings and thoughts head on. While having compassion for yourself that you're suffering like the rest of the world, and being aware of the pain, you can wait for the pain to pass. You can weather these negative emotions. This leads to emotional resilience, and with practice, you become better and better at it.

PROBLEM 4: Being successful

If you think about it, if you see someone more successful than you, and he brags to you about all the things he bought, where you "only" have a run of the mill sedan, it'll be hard to be friends with him.

Therefore, what if you're highly successful, does this mean you'll be disconnected from others? If you have self-compassion, no! As part of humanity (principle 2 of self-compassion), you'll have shared joy.

You are concerned for your own well-being as well as others, so you want both to succeed! By recognizing our inherent connectedness, Dr. Neff writes, "When we're part of a larger whole, we can feel glad that 'one of us' has something to celebrate".

You celebrate with exuberance in the success of others with self-compassion. In fact, with self-compassion, you can genuinely feel that way, instead of grudgingly when you see your friends being more successful than you.

Instead, armed with self-compassion, you become aware of other people's positive traits and fully appreciate them, not taking them for granted. You rejoice in yourself, just as you rejoice in others.

PROBLEM 5: I'm not going to be successful if I have self-compassion

The opposite is true. So many psychological studies have shown that intrinsic motivation is more powerful than extrinsic motivation.

If you're stuck in the self-esteem, need to prove myself trap, you're doing things to be successful, to look smart, athletic so that you can be admired, which strokes your ego. This is extrinsic motivation.

Let's say the activity is very grueling, streaming as a career. With extrinsic motivation, your self-esteem increases when your viewer numbers go up, and then it crashes when your number goes down.

I know this very well. In the early stages of streaming, I actually got depressed when my viewer number went from 10 to 9, WTF!

But you still stream for those numbers because when you grow, your self-esteem does as well, and I did get emotional high's when I got an average of 20 in one month - it's like a drug!

However, during summer, when many are off on vacation, your numbers tend to be lower for the next 3 months. Since you're streaming for self-esteem and those numbers, you may be demoralized and then give up.

Further, by wanting to be successful so you can prove yourself as the "better" streamer (stroking your ego), you're afraid to take creative risks, make mistakes, for fear of losing viewers. You then look "wooden" which is the death-knell in entertainment. You keep to a regular script which can get stale, also another way to make yourself bored and not wanting to stream anymore.

However, if you have self-compassion, which leads to awareness of what you truly want in life, you decide to stream because you love the process as well as your viewers.

You enjoy the connection and the challenge of negotiating chat and gameplay, and finding new creative angles to be entertaining.

This is intrinsic motivation, you're doing something because you want to do it. You don't care if you fail and your numbers drop like flies, because your self-esteem isn't harmed in any way, because you have self-compassion.

If you do something "dumb" while streaming, you'll be able to do your "ouch" mantra, hold the embarrassment, and move on, with emotional resilience. You can take enormous risks (historically leading to major advancements in technology and innovation) because you simply don't care about social rejection or judgment, or low viewer numbers. You are authentic and free.

If you're stuck on an issue with streaming, you're not afraid to ask for help for "fear of looking stupid". In other words, you're not controlled by societal pressures when you have self-compassion.

You do your own thing with utmost courage, authenticity, honesty and integrity, screw the rules! Contrary to what people think, self-compassion isn't "wimpy", but bad-ass! What's more bad-ass than being true to yourself and a "rebel".

At any rate, it appears that those who do something they absolutely love tend to be more successful than someone who's doing it to prove themselves.

When you love something, you never get tired of doing it, to the point where you may have to work on self-care issues such as eating regular meals and getting enough sleep (I'm thinking specifically video gaming).

When you're doing something to prove things, you're going to be demoralized when there's a glitch, a temporary obstacle, and failure, and you may quit altogether.

The person who's spending and practicing that many hours because of the enjoyment will tend to be better at the activity than someone who quits in fits and starts due to obstacles in the way.

I wanted to outline the three components of self-compassion here, and present the major arguments for self-compassion.

There are many exercises in the book that I won't outline here, so if you feel that the concept of self-compassion makes sense and can make a difference in your life, I highly recommend Self-Compassion: The Proven Power of Being Kind to Yourself. 

I found the concept of self-compassion jump started me on being way more pro-active in self-care and fulfilling my specific goals. Perhaps borrow the book from the library or look through the book at the bookstore. Do the exercises that resonate with you. Above all have self-compassion!

Note: I have been including the How of Happiness link in the bottom of all my posts, but I found Dr. Neff's Self-Compassion equally important, so I'll be alternating posts with these books.