Thursday, January 16, 2025

Tagore and Behavioural Economics

Rabindranath Tagore's philosophy and Behavioral Economics may seem like unrelated fields, but they share some interesting connections. Here are a few possible links:

Tagore's Philosophy
1. Humanism: Tagore's philosophy emphasizes the importance of human emotions, experiences, and relationships. Behavioral Economics also focuses on understanding human behavior and decision-making.
2. Holism: Tagore believed in the interconnectedness of all things. Behavioral Economics recognizes that human behavior is influenced by various factors, including social, emotional, and environmental contexts.
3. Intuition and Rationality: Tagore valued the role of intuition in human decision-making. Behavioral Economics also acknowledges the interplay between intuitive and rational thinking in shaping human behavior.

Behavioral Economics Principles
1. Loss Aversion: Tagore's concept of "the fear of loss" is similar to the idea of loss aversion in Behavioral Economics, which states that people prefer to avoid losses rather than acquire gains.
2. Framing Effects: Tagore's emphasis on the importance of perspective and context is reflected in the Behavioral Economics concept of framing effects, which shows how the way information is presented influences human decisions.
3. Social Influence: Tagore's philosophy highlights the significance of social relationships and community. Behavioral Economics also recognizes the power of social influence in shaping human behavior, including conformity, social norms, and peer effects.

Applications and Implications
1. Public Policy: Tagore's philosophy and Behavioral Economics insights can inform public policy design, taking into account human behavior, social context, and emotional factors.
2. Marketing and Consumer Behavior: Understanding the intersection of Tagore's philosophy and Behavioral Economics can help marketers develop more effective strategies that appeal to human emotions, values, and social influences.
3. Personal Development and Well-being: The combined perspectives of Tagore's philosophy and Behavioral Economics can offer insights into human well-being, happiness, and personal growth, highlighting the importance of emotional intelligence, self-awareness, and social connections.

By exploring the connections between Rabindranath Tagore's philosophy and Behavioral Economics, we can gain a deeper understanding of human behavior, decision-making, and well-being.

Wednesday, January 8, 2025

Boiled food and Behavioural Economics

### My Cooking Project

**Background:** Fried foods are not allowed.  

**Objective:** Eat boiled food.  

**Method:**  
I chopped all the vegetables and mixed them well with turmeric, cumin powder, and salt, along with a small amount of oil. I let it sit for 5 minutes. Then, I poured two cups of water into a kettle, added the vegetables and noodles, and turned on the switch. Finally, I served it hot.  

**Result:** Oh, so tasty!  

### How Cooking Vegetables in an Electric Kettle Relates to Behavioral Economics  

Cooking vegetables in an electric kettle can be linked to several principles in **behavioral economics**, particularly those that address decision-making, habits, and health-related behaviors. Below are some relevant theories and concepts:  

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### **1. Nudge Theory**  
**Concept:** Nudge theory, proposed by Richard Thaler and Cass Sunstein, suggests that subtle changes in the environment can influence people’s behavior without restricting their choices.  
**Application:**  
- Choosing to cook vegetables in an electric kettle instead of frying them is a "nudge" towards healthier eating. The ease, speed, and simplicity of this method make it more appealing, reducing the friction of making a healthier choice.  
- Using minimal oil and boiling instead of frying aligns with the principle of nudging towards a low-calorie, nutritious diet without banning or drastically changing eating habits.  

---

### **2. Self-Control and Present Bias**  
**Concept:** Present bias refers to people's tendency to prioritize immediate gratification over long-term benefits, often leading to unhealthy choices like fried food.  
**Application:**  
- Cooking vegetables in an electric kettle is a way to address present bias. It simplifies the process and makes healthy eating easier and quicker, reducing the temptation to default to less healthy fried options that may seem more immediately satisfying.  
- By making boiled food tasty and convenient, this method encourages individuals to maintain self-control and align their immediate actions with long-term health goals.  

---

### **3. Habit Formation**  
**Concept:** Behavioral economics emphasizes the role of small, consistent actions in forming habits that influence long-term behavior.  
**Application:**  
- Regularly preparing vegetables using an electric kettle can become a habit, reinforcing healthier cooking practices. The low effort required to use a kettle helps sustain this behavior over time.  
- Once the habit is established, the cognitive effort required to decide between healthy and unhealthy options decreases, as the default choice becomes healthier.  

---

### **4. Loss Aversion and Cognitive Load**  
**Concept:** Loss aversion refers to people's tendency to avoid losses more strongly than they seek gains. Cognitive load theory suggests that decision-making becomes harder when people are overwhelmed by too many choices or steps.  
**Application:**  
- The perception of "loss" (e.g., sacrificing taste by not frying) is minimized when boiled vegetables are made tasty through spices and seasoning.  
- By reducing the cognitive load involved in cooking (fewer steps, less equipment, and minimal monitoring), the method encourages individuals to stick to healthy eating without feeling burdened or overwhelmed.  

---

### **5. Cost-Benefit Analysis**  
**Concept:** Behavioral economics posits that people weigh costs and benefits, often subconsciously, when making decisions.  
**Application:**  
- Cooking in an electric kettle is cost-effective (less oil, minimal equipment, and reduced electricity usage) and time-saving, making it a high-benefit, low-cost alternative to traditional cooking methods.  
- The health benefits (lower cholesterol, fewer calories) outweigh the perceived "cost" of not eating fried food, making it easier for individuals to adopt healthier behaviors.  

---

### **6. Status Quo Bias**  
**Concept:** People often stick to familiar habits (status quo) even when better alternatives are available.  
**Application:**  
- Cooking in an electric kettle disrupts the status quo of frying food by introducing an easy, convenient alternative. It helps overcome inertia by providing a healthier cooking method that is simple enough to become a new status quo.  

---

### **Conclusion**  
This simple cooking technique demonstrates the practical application of behavioral economics by addressing biases, promoting habit formation, and nudging individuals towards healthier choices. It reduces the cognitive and physical effort needed to adopt healthy behaviors, aligning individual decisions with long-term health goals.

Foundation of Machine learning

Syllabus for Faculty Development Program: Foundations of machine learning in psychology  
Duration: 3 Months
Fees: 6k
Eligibility criteria:Working on research projects using psychological instruments , Basic understanding of psychology. 
Eligibility: Post graduation in psychology and social science related fields.
Learning Outcomes: students will learn principles of machine learning by the end of this course,  will be proficient in using SPSS to perform Supervised and unsupervised machine learning, interpret and report the result, develop skills in visualizing and presenting findings. 
Total marks: 100
Grading Criteria: 
A Excellent: 85 percent and above
B Good:72 to 84 percent 
C Satisfactory:50 to 69 percent 
D Needs improvement: Below 50 percent 

Classes per Week: 3 (1 Hour Each)
Target Audience: Faculty members in psychology and related disciplines
Prerequisites: Basic knowledge of psychology and Should have SPSS in laptop.

Enrollment Requirement: Entrance Examination for course: Interested candidates have to to register by paying 100 Rs for entrance exam. Candidates will be selected based upon the entrance exam result.

Pedagogy: Flipped classroom model, students will be given learning material before the class, hands on experience in class, work at home for assignments.

Foundations of machine learning in psychology 
Unit 1: Introduction to machine learning
Unit 2: Data Preparation for Machine learning with SPSS
Unit 3: Supervised learning
Unit 4: Unsupervised learning 
Unit 5: Project (Weeks 1–3)

Unit 1: Introduction to machine learning

1.1 Definition, types  of Machine Learning
1.2 Key concepts and their  Psychological Applications.
1.3. Differences between traditional statistics and machine learning.

Unit 2: Data Preparation for Machine learning with SPSS

2.1 Variable Transformation: Recoding, Dummy Variable and Standardization 
2.2 Splitting Data Sets: Training and Testing data, Handling missing data,Creating new variables.
2.3 Introduction to exploratory data analysis(EDA) and Data cleaning.

Unit 3: Supervised learning: Logistic Regression 

3.1 Fundamentals of Logistic Regression: difference between linear and logistic regression and applications of psychology.
3.2 Key concepts of Logistic Regression
3.3 Logistic Regression Using SPSS

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Unit 4: Unsupervised Learning: K-Means Clustering
4.1 Overview of unsupervised learning and applications, Mathematical Foundations: K-Means algorithm, Distance Metrics.
4.2 K-Means Clustering in SPSS: Setting up the data and running K-Means clustering, output interpretation.
4.3 Evaluating and interpreting K-Means clustering result 

Unit 5: Project
5.1. Project proposal writing 
5.2. Project on Logistic Regression 
53. Project K-Means clustering 

Assessment Structure
Weekly Assignments: To reinforce theoretical and practical knowledge.30 percent, per paper 6 marks 
Final exam : 40 percent  To test understanding of core concepts.
Final Project: 30 percent for  Comprehensive project showcasing the integration of machine learning techniques in psychology.

This structured syllabus ensures systematic learning and application of machine learning techniques in psychological research. The inclusion of practical sessions, project work, and theory-driven discussions makes it ideal for faculty development.








Saturday, January 4, 2025

Machine learning by SPSS

Here’s a step-by-step example of applying Machine Learning (ML) using SPSS to classify participants into groups based on psychological data:


Example Project: Classifying Participants into Stress Levels

Objective

To use SPSS to predict whether participants fall into "High Stress" or "Low Stress" groups based on survey responses.

Dataset

Imagine you have a dataset with the following columns:

  1. Age (numerical)
  2. Hours of Sleep per Night (numerical)
  3. Number of Stressful Events in the Last Month (numerical)
  4. Self-Reported Stress Level (categorical: "High" or "Low")

Steps in SPSS

1. Load the Data

  • Open SPSS.
  • Import the dataset (e.g., in .sav or .csv format).
  • Ensure columns are labeled correctly and data is clean.

2. Data Preparation

  • Recode variables if needed: For example, ensure "High" and "Low" in the stress level column are coded as 1 and 0 for binary classification.
  • Check for missing values and handle them (e.g., replace with mean/median or exclude cases).
  • Normalize numerical variables if needed (optional).

3. Exploratory Data Analysis (EDA)

  • Use Descriptive Statistics to understand the data distribution.
  • Create Boxplots or Scatterplots to explore relationships (e.g., stress vs. sleep hours).

4. Train a Decision Tree Model

  1. Navigate to Analyze > Classify > Decision Trees.
  2. Set the dependent variable to "Stress Level" (High/Low).
  3. Set the independent variables to Age, Sleep Hours, and Stressful Events.
  4. Choose the CHAID or CART algorithm for the decision tree.
  5. Configure stopping criteria (e.g., minimum number of cases per node).
  6. Click OK to run the model.

5. Interpret the Results

  • SPSS generates a decision tree diagram showing how variables split to classify participants.
  • For example:
    • If "Stressful Events > 5," classify as "High Stress."
    • Otherwise, check "Hours of Sleep < 6" to classify as "High Stress."

6. Evaluate the Model

  • Look at the classification table to assess accuracy, sensitivity, and specificity.
  • Analyze the confusion matrix to see how well the model predicts "High" vs. "Low" stress.

7. Use the Model for Prediction

  • Apply the model to a new dataset using the Scoring Wizard in SPSS.
  • Generate predicted stress levels for new participants based on input features.

Outcome

  • Students learn to build a simple decision tree model in SPSS.
  • They interpret results and understand practical applications of machine learning in psychology.
  • Real-world application: Predicting stress levels for psychological interventions.


Thursday, January 2, 2025

Course design

RPRIT aims at the followings:
1. *Develop research skills*: Provide researchers with the necessary skills and knowledge to conduct high-quality research.
2. *Enhance research capacity*: Build the capacity of researchers to design, implement, and disseminate research findings.
3. *Foster collaboration*: Encourage collaboration among researchers from diverse backgrounds and disciplines.
4. *Promote knowledge sharing*: Facilitate the sharing of knowledge, expertise, and best practices among researchers.
5. *Support career development*: Help researchers advance their careers by providing them with the skills and knowledge needed to succeed in their field.

Some potential course topics could include:

1. *Research methodology*: Study design, data collection, data analysis, and interpretation.
2. *Statistical analysis*: Introduction to statistical software, data visualization, and statistical modeling.
3. *Academic writing*: Writing research papers, grants, and reports.
4. *Research ethics*: Ethics in research, informed consent, and data protection.
5. *Project management*: Managing research projects, timelines, and budgets.
6. *Communication skills*: Presenting research findings, creating posters, and designing visual aids.
7. *Interdisciplinary research*: Collaborating across disciplines, integrating different methodologies, and addressing complex research questions.

By offering these courses, the institute can support the development of researchers and enhance the quality of research conducted.