The Censorship Game in communication theory shows us the complexities of shared information, especially when vested interests (like profit) are at stake. In the case of pollution, transparency laws and public accountability are key to balancing these interests. The END. 13/13.

However, there’s a trade-off: sometimes, censorship can aim to avoid panic or prevent economic disruptions. The question is about the line between protective censorship and harmful information control. Economics helps us model and analyze these impacts. 12/13.

In the Censorship Game, there’s a constant balance (or struggle) between the sender’s obligation to report truthfully, the censor’s choice on what to allow, and the receiver’s right to full information. Transparency advocates push for policies to limit such censorship. 11/13.

In economic terms, this creates a problem of information asymmetry where the sender holds more accurate info than the receiver. With censorship, the asymmetry widens, leading to decisions that don’t account for the true costs and risks—like those of pollution. 10/13.

This censorship can lead to severe inefficiencies and misinformed decisions. The public may continue to use polluted water, risking health issues, while the factory avoids accountability. This ultimately harms the receiver, who cannot act on accurate information. 9/13.

Scenario.The factory releases a report claiming its pollution is “within safe limits.” However, the real data suggests high pollution levels. The censor opts to present a diluted message to avoid triggering public backlash. The Receiver remains unaware of the true danger. 8/13.

In the Censorship Game, the factory, censor, and public each have their own incentives. The factory wants profit, the censor might want to balance public perception, and the public wants transparency. Conflicts arise when incentives misalign. 7/13.

In our pollution example, the Censor could choose to reduce or “soften” data about pollution levels to minimize public outcry or avoid penalties. The receiver then gets an incomplete or altered version of the message. This alters the communication dynamic significantly. 6/13.

In a world without censorship, the factory sends pollution reports to the authorities and the public directly, enabling transparency. However, in The Censorship Game, a Censor (such as the government) may modify or block parts of this information. 5/13.

For an example: imagine a factory polluting a local river. The factory (Sender) has information about the pollution levels and their impact on health and the environment. Nearby residents and local authorities (Receivers) rely on this info to make informed decisions. 4/13.

In communication theory, the Sender is the party sharing information, while the Receiver is the one intended to receive and interpret it. But what if a Censor decides some messages are too “sensitive” or harmful to reach the receiver? This is where the censorship layer comes in.

Let’s break down how communication works between a Sender and a Receiver in economic theory, especially when there’s censorship at play. This is known as the Censorship Game, where the sender’s message may be blocked or altered based on the message's content. 2/13.

🧵𝐓𝐡𝐫𝐞𝐚𝐝 𝐨𝐧 𝐓𝐡𝐞 𝐂𝐞𝐧𝐬𝐨𝐫𝐬𝐡𝐢𝐩 𝐆𝐚𝐦𝐞 🧵.1/13.

So, what did we learn? Time series modeling is super handy, especially for forecasting. SARIMA = ARIMA + seasonality, making it ideal for real-world, cyclical data! 🔄💡.

Both models help capture trends, but SARIMA shines when data has cycles. After tuning parameters for both models, we can compare performance using metrics like RMSE or AIC.

SARIMA adds a seasonal layer, accounting for trends that repeat at regular intervals (like months or years). It’s perfect for data like Apple’s stock prices, where we see seasonal patterns.

But before applying ARIMA, we must check if the data is stationary. How? With an ADF Test (Augmented Dickey-Fuller)! If the series isn’t stationary, we apply differencing. 📉.Now, SARIMA! It’s ARIMA’s cool sibling that handles seasonality.

It’s a model used for time series forecasting. ARIMA has 3 key components:. AR: Looks at past values to predict the future (auto-regression). I: Makes the data stationary through differencing. MA: Uses past forecast errors to improve predictions.

Have you ever wondered how we can predict stock prices?.Today, let’s break down two powerful forecasting methods: ARIMA and SARIMA. We’ll be using Apple’s stock price data!.First up, ARIMA stands for AutoRegressive Integrated Moving Average.

🧵 𝐓𝐡𝐫𝐞𝐚𝐝: 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 𝐀𝐩𝐩𝐥𝐞 𝐒𝐭𝐨𝐜𝐤 𝐏𝐫𝐢𝐜𝐞𝐬 𝐔𝐬𝐢𝐧𝐠 𝐀𝐑𝐈𝐌𝐀 & 𝐒𝐀𝐑𝐈𝐌𝐀 📈.