Real-Time Naughty-Nice List Updating Procedure with Post-Modern Graph-Based Morality Adjustments

Santa Claus1 Twinkles Holly-Jolly Tinselbottom2, Peppermint Glitter-toes III 3

1 Head Toy Delivery Executive, Kringle Enterprise, North Pole

2 Head of R&D Department, Kringle Enterprises, North Pole

3 Philosophy Department Head, Santa Claus State University, North Pole

Abstract

Checking the list twice is no longer sufficient in our modern world of moral ambiguity. What is considered right and wrong changes by the day, it has become increasingly difficult to categorize the world’s population as Naughty or Nice (NoN). Despite some NoN criteria being written into law, neutral behavior requires more context dependent on social customs or conditions unique to the potential Gift or Coal Recipient (GCR). While it may be easy to judge a GCR by a traditionally objective NoN assessment and apply a Constant False Alarm Rate (CFAR) detection scheme, analysis and simulation discussed in this paper suggests that a first, second, and third order NoN state estimate may provide better results to enforce better behavior in all the boys and girls out there while additionally filtering out high risk Type I/II errors. A Christmas Spirit Risk (CSR) minimization is found to favor estimates in second and third NoN state estimates more than the traditional first order. Previously limited to checking the list only twice a year, a real-time update scheme reinforced by a global surveillance state handed over to us by the CCP’s generous data tracking via Tik Tok, this paper will provide a comprehensive algorithm which is estimated to increase good behavior by 42% with only a modest 4% increase to the required toy production logistics chain.

Keywords: Naughty-Nice List, Christmas, Gift Delivery Error, Signal Detection Theory, Ensemble Morality Model

1. Introduction

In ethics, the naughty-nice list is one of the most under studied fields despite the fact that judging people is the most common day to day ethical practice. This perhaps may be because judging people too much will put you on the naughty list. Many major religions might preach total depravity as shown in the Christian passage “for all have sinned and fall short of the glory of God” –Romans 3:23 and avoid the question of who’s good or bad altogether. Others will adhere to total acceptance in some new age humanistic belief about how we’re all harmonizing with the greater conscious of the universe or a Taoist idea of harmony with each other. None of these are useful in terms of determining which boys and girls get presents and which fall short of the glory of Santa [1].

In the philosophy of Santa Claus, there are naughty children and there are nice children. While what makes a child Naughty or Nice (NoN) may seem subjective, with the correct formulation based in cultural relativism, a NoN detection is much easier to solve using signal detection theory [2]. As shown in the confusion matrix below, there are two types of children, two different decisions to make on Christmas, and four outcomes: two naughty and two nice.

Figure 1: Naughty-Nice Confusion Matrix.

2. Background

In order to understand the complexities of the NoN estimation problem and the solution presented in this paper, one must understand the history of the NoN list, its legalistic constraints and signal detection theory.

2.1 Naughty Nice List History

The Naughty-Nice list is the oldest living document stretching back to the fourth century. It has been continuously updated to reflect the moral standing of all Gift or Coal Recipient’s (GCR) for nearly two Millenia. Originally applying a subjective personal estimate on each GCR, the Second Council of Nicaea (787) instituted a “Great Tome of Behavioral Conduct” to dictate what constituted good or bad behavior. It was determined that the Tome could be amended with 2/3rds majority of church Bishops [3]. In this same council, all pagan witch magic and all-seeing wizard orbs were confiscated by the church and gifted to Santa to measure good Christian behavior throughout the year. With a strict basis in biblical law, the Tome rarely changed. Notable changes occurred in the 1215 Magna Carta when the legal right of Habeas Corpus extended to a required two list checks per year [4]. Finally, soon after the end of the Black Plague in 1356, hand washing and covering your mouth when you cough or sneeze, among other non-hygienic sins were soon added to the Tome as good behavior.

Fig 2: Botticelli’s Depiction of Santa Reading the Great Tome of Behavior at the Second Council of Nicaea.

As the world shifted away from a religious monoculture, so did the Tome and the list update procedure. Administration in East Asian society began to follow a long bureaucratic process instituted by elves sharp enough to pass a civil service exam. GCR behavior in Islamic ruled nations began applying their own moralistic codes from the Qur’an and others from the Veda’s in Hindu cultures as the Christmas tradition began to globalize [5]. With the religious reformation, even the European Tome began to bifurcate more into regional areas. It wasn’t until the early 19th century when the list update protocol secularized.

Beginning with Enlightenment philosophy, escalating with a climactic series of revolutions, and ending with the Christmas constitution of 1848 after a legal dispute with the Habsburgs, the Constitutional Republic of the North Pole separated all ties with regional governmental powers or organized religion [6]. After Santa Claus transitioned from the protectorate to an elected executive power role in the elections of 1850, the North Pole and the resulting NoN update procedure has been stabilized as Santa has been consecutively re-elected every 4 years due to his nonstop blow out election results. Despite certain universal bad behaviors being strictly enforced such as murdering, stealing, or getting tattoos, Santa has found freedom in the ability to prescribe a unique morality for each culture, town, or even at a GCR level.

2.2 Signal Detection Theory

While historically, NoN detections were treated completely objectively with a threshold set by a simple majority of Catholic Bishops or ecclesiastical equivalents of varying cultures, the new modern NoN detection framework creates a hypothesis that a GCR is either Naughty or Nice, then treats the behavior as a signal to infer the current state of that GCR [7]. Suppose that a GCR is either Naughty or Nice which produces two different types of behaviors as shown in the figure below. The detected signal will be the average sum of scored behaviors as discussed in [8]. Applying the Central Limit Theorem on at least 25 measured behaviors, the distribution may be assumed as normal. All measured behaviors are weighted according to [9]. Even among the most noisy and random behaviors from toddler’s [10], an estimate may be made with enough behavior measurements.

Fig 3: Naughty-Nice Signal Detection Distributions.

Dependent on the context of the GCR’s life and culture’s relativistic morality the statistical difference may be labeled as the Naughty-Nice D prime (NNDP) similar to a Signal to Noise Ratio (SNR). Given the decision criterion, the green region shows the correct assessment rate, the red represents the correct rejection rate, light blue shows the type I error, and the dark blue shows the type II error. Unfortunately for our annual list checking procedure, our NNDP sensitivity has been shrinking causing an enormous amount of error and an increase in virtue signaling [11].

While the behavioral reinforcement effect of higher order NoN metrics has been known for centuries [12], checking the Naughty-Nice List Twice is insufficient to provide higher order NoN estimation and a simple Constant False Alarm Rate (CFAR) detection scheme has been implemented to choose the Criterion. Behaviors are measured randomly and processed bi-annually.

2.3 Metrics

Gift Delivery Error (GDE): A GDE is the act of gifting a present to a naughty GCE. This is statistically treated as a type I error. A GDE is typically weighted at a lower risk as a CDE as treating people nicely increases Santa’s own behavioral score and usually can turn around a Naughty GCE’s Christmas spirit around.

Coal Delivery Error (CDE): The CDE is the act of gifting coal to a nice GCE. This is statistically treated as a type II error. Though not every CDE will be treated the same, they are historically given a higher negative cost than other errors as it can cause some to lose hope.

Christmas Spirit Quantified Risk (CSQR): The CSQR measures and estimates the total effect on Christmas Spirit in the world given a correct or incorrect NoN detection. In order to quantify the actual risk, the CSQR was developed to quantify positive and negative benefits not only to Christmas but human wellbeing [14]. According to unbiased research funded by Kringle Enterprises, a positive increase in Christmas spirit always correlates to positive human well-being by spreading good tidings and cheer.

Fig 4: Example negative 38 (child receiving tube socks) and positive 183 (child receiving Legos) CSQR’s.

Good/Evil: Reviewer Number 2 of this paper required that we define Good and Evil and would not let us get away without defining it even though I’m Santa and their boss. After extensive research, we elected to go with a blend shown in the table below. We determined in an extensive study [15] that no model could define what good or evil was and determined to create an ensemble model based off of historic data. These variables will be used for the Kalman filter NoN state space to be later discussed. The weights of the model were determined by measuring every college philosophy paper ever and negatively weighting by each author’s future criminal record.

Ethical DefinitionVariablePercentGolden RuleGR32%Suffering MinimizationSM23%Virtue EthicsVE18%Deontological EthicsDE14%Gut FeelingGF8%ExistentialismE4%UnknownX1%Table 1: Ethical Ensemble Model Results.

3. Postmodern Morality Adjustment

After the horrors of the 20th century, Santa is not sure if objective truth or morality actually exists. It is certainly clear that Good and Evil exists, but the truth behind that is now unclear. Accordingly, a structured method discussed in this section will create a method to determine a GCR’s true character without objective truth. Judging most ethical work circular and unhelpful we’ve continued to elect to leverage scientific methods for these adjustments.

3.1 Moral Enthalpy Model

By using the heat equation and thermal dynamics, we were able create an enthalpy model for morality. While moral entropy is thrown around willy-nilly, it’s about time we look at moral enthalpy where we observe the internal morality sources in the same way that engineers measure and model internal heat sources for decades. Using the equation below, and buttload of partial differential equations, the internal morality of a GCE may be measured by measuring the diffusion of morality around a GCE using the ensemble Good/Evil model in table 1. H represents the measured moral enthalpy while U is the internal moral good or bad, row represents the social pressure of the volume of a friend group V. For instance, if someone is bullied into TODO online by a high social pressure p, then little enthalpy will be measured.

3.2 LLM Adjusted Moral Framework

Spying on humanity for 16 centuries has an obvious benefit, it produces enough data to train a Large Language Model (LLM). We have humbly named our LLM Claus GPT after the two things that made it possible: Santa and Transformers. Using LLM’s we’ve been able to automate much of the deliberation process in contextualizing the morality judgement of moral action. This has proven invaluable to the through put of the less clear-cut scenarios. Previous experiments in outsourcing moral judgment to the subreddit r/AmITheAsshole was found to have a biased Beta value far outweighing a Naughty judgment over a nice judgement [15]. While ClausGPT wasn’t found to handle most of the tricky cases, it filtered out many of the easy ones.

3.3 Graphical Morality

Contextualizing moral actions into a social framework additionally complicates the NoN estimation process. If graphical solutions can explain what’s insane with Facebook [16], or how to avoid your Ex [17]. As shown in the figure below, a moral action is often required to be contextualized in the social circumstance. For example, certain racialized or sexualized words or actions would be considered despicable in the majority of cases while would be considered wholesome and endearing in another. From a study regarding a way too real-world example that broke our detection algorithm four years ago [18], in the figure below, the act of a heterosexual male slapping another heterosexual male on the posterior develops a more positive posterior moral update when the graphical structure is tight in the left clique. In the right clique, the moral posterior update is negative due to a looser social connection between the exact same heterosexual male slapping a heterosexual male on the posterior. The context may even become worse if the posterior slapper has priors. This contextualization will be the basis of creating the measurement transform Matrix H to be used in our Kalman filter.

Fig 5: Example Graphical Clique Adjustment of Moral Actions.

4. Real-Time Kalman Filter Update Procedure

This year’s post-Christmas analysis determined that the primary cause of estimation error was the NoN estimation’s inability to track higher order NoN state changes. Two checks per year may fulfil the agreement of the Magna Carta’s Writ of Habeas Corpus but it is not nearly enough estimates to determine a change in moral character over time. In order to maximize the CSQR, it doesn’t matter where the GCR is six months ago but on Christmas. Modern Santa requires a model which supports time extrapolation!

The new procedure this year will include second and third order NoN state space extrapolations implemented through a 0.2mHz behavioral check. We tried to accomplish this last year with our increased level of behavioral data but completely crashed our computing cluster by fitting the 18-Dimensional state space with a single linear regression on every human on the planet’s year worth of data the night before Christmas to include all the data. This year we have been using a Kalman Filter to update the state over time and our cluster hasn’t crashed yet.

4.1 NoN-State Space

The NoN state space was developed using the ensemble model previously described. By adding the first and second derivative the resulting state space may be shown in the equation below. After a previous analysis, third order moral state estimates only favored jerks. We wanted to include the X-factor but had no idea how to calculate the Jacobian H-Matrix even numerically.

4.1 Prediction Step

Before applying a new measurement, the state of someone’s soul must be projected forward to the current time. To do this, a State transition matrix F will be applied to both the NoN state and the NoN covariance P while adding in a Control input matrix B and process noise Q to account for those out of the box personalities who won’t fit our ensemble morality model.

4.2 Correction Step

After sampling behavior z, a gain factor k may be estimated using the covariance and measurement transformation matrix H which applies the contextualized moral action from section 3.3. Next using the gain factor and the predicted measurement estimated by Hx, the difference in measured vs expected behavior is used to update the NoN state X. Naturally, if the Mahalanobis distance of the measurement if more than eight standard deviations away from the state estimate given the current P, that behavior is rejected and binned for the Interactive Multiple Personality Model to be explained in section 4.4.

4.3 Covariance Update

Finally, the NoN state covariance P may be adjusted using the resulting K and H matrix as shown in the equation below.

4.4 Interactive Multiple Personality Model

People are noisy and rarely fit a perfect model. While the18 state NoN model will make it easier to detect inflection points in people’s lives making a turn for the better but without an Interactive Multiple Personality Model (IMPM), an extrapolation could misinterpret one good day as a positive inflection or worse bad day as a downward spiral. For each GCR filter, simultaneous models will be tracked and weighted using a Markov chain. In the eventuality that many behavior measurements z are being rejected by our rejection filter discussed in 4.2, this may become the basis of a new tracked personality of the GCR. A stable 6 state, a 12 state, and an18 state will be tracked and filtered for each GCR for any one time-step. Given the fit to each model, any one extrapolation will weight each model estimate fused into the fateful Christmas Eve update and fusion estimate to determine the NoN estimate.

Many major life changes can happen around the Christmas season as discussed in the Scrooge Study [19]. Though someone may regularly be on a steady moralistic trajectory, there is always the realistic scenario, often involving ghosts, that they may be going through a life altering transformational journey like Scrooge analyzed in [19]. Likewise, in the condition of a sudden downturn of hope the night of Christmas as examined in the Wonderful Life George Bailey study [20], such an IMPM could penalize a man who deserves nothing but the best in his last moments before Christmas. The implementation of this model should only be executed with caution. Nice to Naughty detection will be carefully observed and the type II error will be weighted much less over a high type II error weight when considering higher order noise of maintaining IMPM’s.

5. Implementation

To make this plan a reality, a high sampling automated surveillance system, an extrapolation procedure, and a massive database expensively maintained by Oracle will be used.

5.1 Global Surveillance System

Even though we still maintain the Wizard pondering orbs gifted to us from the second counsel of Nicaea and proven to be reliable, particularly when coupled into a massive recording and database infrastructure called Santa-Vision [21]. Gaining daily behavioral measurements for the entire world population requires even more data collection and cleaning than the orbs and free elf labor can provide. The top cyber security red team of elves has been tasked to break into the TikTok servers to collect and gather behavioral data on the global population. Given the amount of data collected in that app, it is more than enough to obtain a daily behavioral sample on every GCR other than the perpetually offline and a large unnamed population of East Asians who seem to be using a VPN and are hard to trace [22].

Fig 6: Santa-Vision global surveillance headquarters of elves spying on GCR’s.

5.2 List to Toy Prediction

As Christmas time draws near, our models will extrapolate GCR state estimates to Christmas day. Each GCR state will then be ranked for likeliness of a positive nice estimate and those states most wished for gifts will be prioritized and mapped. For the GCR states on the fence, a stockpile of generic stocking stuffers to include candy, fidget toys, Rubik’s cubes, stress balls stationary kits, underwear, card games, socks, underwear, and Red Lobster/Block store gift cards are accumulated to take up slack for the last-minute nice children who just barely make the cut. Whatever is left over will end up spread across the better GCR’s stockings as additional presents. This will be mapped by tracking monetary value to how good the children were.

5.3 Criterion Selection

Each criterion will be selected for each GCR based on the estimated risk of drop in CSQR. The equation below will be optimized to the equation below based on the uncertainty tracked in each GCR state estimate. In the CSQR total equation, a positive outcome will be represented by a positive risk and vis versa. All CSQR’s will be negative for GDE’s and CDE’s while only some will be negative for Correct Naughty detections.

Each criterion will be selected for each GCR based on the estimate of the ensembled IMPM. The corresponding CSQR’s will weigh the associated distributions which will likely adjust the detection Criterion. As a graphical representation, the three distributions below show how a normal NoN state distribution may shift the Criterion as the relative CSQR’s affect as the overall Christmas Spirit optimization problem.

Fig 7: A left shifted Criterion for GCR’s who will be more likely to increase CSQR when receiving gifts and scarred by receiving coal.

Fig 8: GCR’s who are respond more to the stick than the carrot and are more likely to get coal or only tube socks.

6. Results

In a test program, the new NoN detection method was trialed on two double blind placebo “Christmas in July” groups in New Zealand who preferred Christmas lined up with their winter solstice. Over the previous six months, the effect on each group’s Christmas spirit and gift-coal receiving actions were monitored through the Santa-Vision and TikTok to score the recorded detections. As shown in the before and after Gift Receiver Operating Curve (GROC) plots below, the advances in the detection algorithm appeared to greatly improve performance with an increased Area Under Curve (AUC).

After the data was compiled, a surprise was revealed. We tested the testers using a triple blind study. North Pole’s data science group was tested to ensure there was no positivity bias by giving several groups mislabeled and correctly labeled data sets to not only the elves analyzing the data but to the elves cleaning the data. Ever since it was observed that R&D elves often put their thumb on the scale to find significant results so that they wouldn’t be demoted into quality assurance [23], the triple blind study has become necessary. It was then determined in the comparison GROC plots below that there is a slight positivity bias in our data science division but not enough to cause any serious alarm. The resulting triple blind study plots are shown below.

Fig 9: GROC Plot for last years and the Christmas in July Study.

Finally, a logistical toy production analysis was completed to show that despite overplanning for more potential gift giving, the advanced predictive capability of the higher order model allowed for better present demand forecasting. Overall, elf productivity was only required to increase by 4%.

7. Discussion

It is difficult to determine any truth data. A child receiving coal on Christmas morning will throw the same tantrum whether they are naughty or nice. They will also tend to get the message and improve their behavior before the next Christmas season. Likewise, if measure-able behavior is improved only to receive presents, does that change whether or not a child is truly naughty or nice? Or is it just an annual contractual agreement between the children of the world and Kringle Enterprises. Previous research suggests maybe [24].

With a new emphasis on behavioral modification and reinforcement through Christmas gifts instead of shaming the misbehaving children or traumatizing through Krampus visits, the spirit of Christmas has evolved in the modern age to be something more than it could have been. In the early days, only the good children would receive presents because elf industrial throughput was so poor, particularly when instituted through the slow and innovation resistant guild system [25].

Now with a fully mechanized toy factory and an interdimensional portal providing infinite raw materials [26], the excess of modern society allows us the opportunity to choose whether or not to shame children for misbehaving. As Christmas gift reinforced behavior is further studied, we may learn more about whether or not a child’s NoN state truly is static or adapts to its environment.

8. Conclusion

The results of this paper will revolutionize the Naught-Nice list update procedure. There hasn’t been a development this significant since the forming of the constitution and the Elf’s Republic of the North Pole was formed in the mid 19th Century. Not only is the method expected to improve toy production logistics, but as global surveillance and behavioral detection measurement error improves, the real time update procedure will improve and become further automated. Christmas is about to get a whole lot merrier. Happy Christmas to all, and to all a good-night!

References

1. Claus, Santa, David “Milk n’ Cookies” Snowflake, et Al 1973 Philosophical Treatise of Half-Moral Depravity :: Journal of Christmas Ethical Philosophy
2. Claus, Santa, Tinkles MacGoo 2017 An Optimal Signal Detection Test Procedure for Naughty-Nice Estimation :: Annals of Christmas Mathematical Theory
3. Claus, Santa, Ted the Elf 2015 An Abridged History of Christmas Vol 1 Way Past :: Chronicles of Christmas History
4. Claus, Santa, Ted the Elf 2016 An Abridged History of Christmas Vol 2 Past :: Chronicles of Christmas History
5. Claus, Santa, Ted the Elf 2017 An Abridged History of Christmas Vol 5 Post-Renaissance :: Chronicles of Christmas History
6. Claus, Santa, Ted the Elf 2015 An Abridged History of Christmas Vol 27 The Age of Revolution :: Chronicles of Christmas History
7. Claus, Santa, Gregsparckle U. Hilbert 2013 A Reconstruction of the Naughty-Nice Problem: A state based model :: Journal of Christmas Detection and Estimation
8. Claus, Santa, Pinçuishon Huliée-Lêgoumes, et Al. 2018 A Statistical Study of Naughty-Nice Behavior :: Journal of Christmas Statistical Studies
9. Claus, Santa, Horkins Hawludday-Hamm et Al. 2017 Behaviormetrics: A Systematic Approach to Judging Everyone on the Earth and Kepton B :: Journal of Christmas Judgement
10. Melba McCormick, 2022 Novel Techniques for Random Number Generation: Toddler Behavioral Sampling :: Chapter 12: Et al. Because not all research Deserves a Nobel Prize
11. Claus, Santa, Jingle-Bells Sparkle Prince III et Al. 2022 Virtue Signaling Additive Noise in Naughty-Nice Detection :: Annals of Christmas Social Modeling
12. Claus, Santa, Kleon Toysmith 2017 Naughty-Nice Higher Order State Estimation: Can the Naughty Improve and how Fast :: Journal of Christmas Tracking Systems
13. Claus, Santa, Kevin Mistle-Toe-Johnson 2017 The Christmas Spirit Quantified Risk as a Common Sense Metric for Gift-Coal Delivery Error Optimization :: Journal of Christmas Econo-Metrics
14. Claus, Santa, Omnerflough Tree-lights 2021 An Ensemble Model of Morality: Screw Treatises, let’s just Average it :: Journal of Christmas Moral Philosophy
15. Claus, Santa, Carol Caroler 2022 Can Reddit Solve the Elf Labor Crisis: Outsourcing Methodologies for Naughty-Nice Detection and Monitoring :: Journal of Christmas Economics and Elf Labor
16. Zark Muckerberg 2022 A Loopy Belief Propagation Factor Graph Simulation of my Grandma Nonna’s Insane Facebook Feed :: Chapter 17 Et al. Because not all Research Deserves a Nobel Prize
17. Chad Broman 2023 Breaking Up with your Girlfriend bt not your Friends: A Cyclic Graph Algorithm for Social Network Preservation :: Journal of Astrological Big Data Ecology
18. Claus, Santa, Mary Christians 2016 An Iterative Updating Procedure for Clique Based Morality Structures: A Solution to the Trevor Incident :: Annals of Naughty Nice List Procedures
19. Claus, Santa, Mary Christians 1904 The Scrooge Naughty Nice Detection Outlier: Christmas Ghost Induced Change of Life Inflection Points :: Annals of Naughty Nice List Procedures
20. Claus, Santa, Mary Christians 1953 It’s a Wonderful Estimate: How one Bad Night Almost stole George Bailey’s Christmas Present :: Annals of Naughty Nice List Proceduresi
21. Claus, Santa, Agent Gumdrop 2005 A Global Surveillance Methodology and Modernization of Wizard Orb Data Management :: Journal of Christmas Intelligence
22. Claus, Santa, Agent Gumdrop 2022 TikTok Limitations in Global GCR Naughty-Nice Surveillance :: Journal of Christmas Intelligence
23. Claus, Santa, Sugar “Sprinkles the Elf” 2019 A Triple Blind Study Solution to the Elf Research Positivity Bias :: Journal of Christmas Data Science Excellence
24. Claus Santa, Peppermint Glitter-Toes III 2015 Are Kids just Acting Nice for Presents? :: Journal of Christmas Philosophical Musings
25. Claus, Santa, Ted the Elf 2018 An Abridged History of Christmas Vol 32 The Industrial Revolution :: Chronicles of Christmas History
26. Claus, Santa, Ted the Elf 2019 An Abridged History of Christmas Vol 73 The First Infinite Portal Christmas :: Chronicles of Christmas History

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