You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


#!/usr/bin/env python3

# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from parlai.core.worlds import World
from parlai.mturk.core.agents import MTurkAgent


class MTurkDataWorld(World):
    def prep_save_data(self, workers):
        """
        This prepares data to be saved for later review, including chats from individual
        worker perspectives.
        """
        custom_data = self.get_custom_task_data()
        save_data = {'custom_data': custom_data, 'worker_data': {}}

        for agent in workers:
            if isinstance(agent, MTurkAgent):
                messages = agent.get_messages()
                # filter out peer feedback
                save_messages = [
                    m for m in messages if m.get('text') != '[PEER_REVIEW]'
                ]
                save_data['worker_data'][agent.worker_id] = {
                    'worker_id': agent.worker_id,
                    'agent_id': agent.id,
                    'assignment_id': agent.assignment_id,
                    'messages': save_messages,
                    'given_feedback': agent.feedback,
                }

        # In simple pairing case, attach the feedback right here
        if len(workers) == 2 and all([isinstance(w, MTurkAgent) for w in workers]):
            data = save_data['worker_data']
            a_0 = workers[0]
            a_1 = workers[1]
            data[a_0.worker_id]['received_feedback'] = a_1.feedback
            data[a_1.worker_id]['received_feedback'] = a_0.feedback

        return save_data

    def get_custom_task_data(self):
        """
        This function should take the contents of whatever was collected during this
        task that should be saved and return it in some format, preferrably a dict
        containing acts.

        If data needs pickling, put it in a field named 'needs-pickle'
        """
        # return {
        #     'acts': [self.important_turn1, self.important_turn2]
        #     'context': self.some_context_data_of_importance
        #     'needs-pickle': self.json_incompatible_object
        # }
        pass


class MTurkOnboardWorld(MTurkDataWorld):
    """
    Generic world for onboarding a Turker and collecting information from them.
    """

    def __init__(self, opt, mturk_agent):
        """
        Init should set up resources for running the onboarding world.
        """
        self.mturk_agent = mturk_agent
        self.episodeDone = False

    def parley(self):
        """
        A parley should represent one turn of your onboarding task.
        """
        self.episodeDone = True

    def episode_done(self):
        return self.episodeDone

    def shutdown(self):
        """
        Clear up resources needed for this world.
        """
        pass


class MTurkTaskWorld(MTurkDataWorld):
    """
    Generic world for MTurk tasks.
    """

    def __init__(self, opt, mturk_agent):
        """
        Init should set up resources for running the task world.
        """
        self.mturk_agent = mturk_agent
        self.episodeDone = False

    def parley(self):
        """
        A parley should represent one turn of your task.
        """
        self.episodeDone = True

    def episode_done(self):
        """
        A ParlAI-MTurk task ends and allows workers to be marked complete when the world
        is finished.
        """
        return self.episodeDone

    def shutdown(self):
        """
        Should be used to free the world's resources and shut down the agents.

        Use the following code if there are multiple MTurk agents:

        global shutdown_agent
        def shutdown_agent(mturk_agent):
            mturk_agent.shutdown()
        Parallel(
            n_jobs=len(self.mturk_agents),
            backend='threading'
        )(delayed(shutdown_agent)(agent) for agent in self.mturk_agents)
        """
        self.mturk_agent.shutdown()

    def review_work(self):
        """
        Programmatically approve/reject the turker's work. Doing this now (if possible)
        means that you don't need to do the work of reviewing later on.

        For example:
        .. code-block:: python
            if self.turker_response == '0':
                self.mturk_agent.reject_work(
                    'You rated our model's response as a 0/10 but we '
                    'know we\'re better than that'
                )
            else:
                if self.turker_response == '10':
                    self.mturk_agent.pay_bonus(1, 'Thanks for a great rating!')
                self.mturk_agent.approve_work()
        """
        # self.mturk_agent.approve_work()
        # self.mturk_agent.reject_work()
        # self.mturk_agent.pay_bonus(1000) # Pay $1000 as bonus
        # self.mturk_agent.block_worker() # Block this worker from future HITs
        pass


class StaticMTurkTaskWorld(MTurkDataWorld):
    """
    World for handling generic tasks that aim to use ParlAI as an MTurk interface, but
    don't need the server to be in the loop.
    """

    def __init__(self, opt, mturk_agent, task_data):
        """
        Init should be provided with the task_data that the worker needs to complete the
        task on the frontend.
        """
        self.mturk_agent = mturk_agent
        self.episodeDone = False
        self.task_data = task_data

    def did_complete(self):
        """
        Determines whether or not this world was completed, or if the agent didn't
        complete the task.
        """
        agent = self.mturk_agent
        return not (agent.hit_is_abandoned or agent.hit_is_returned)

    def episode_done(self):
        """
        A ParlAI-MTurk task ends and allows workers to be marked complete when the world
        is finished.
        """
        return self.episodeDone

    def parley(self):
        """
        A static task parley is simply sending the task data and waiting for the
        response.
        """
        self.mturk_agent.observe(
            {'id': 'System', 'text': '[TASK_DATA]', 'task_data': self.task_data}
        )
        self.response = self.mturk_agent.act()
        self.episodeDone = True

    def prep_save_data(self, workers):
        """
        This prepares data to be saved for later review, including chats from individual
        worker perspectives.
        """
        custom_data = self.get_custom_task_data()
        save_data = {'custom_data': custom_data, 'worker_data': {}}

        agent = self.mturk_agent
        save_data['worker_data'][agent.worker_id] = {
            'worker_id': agent.worker_id,
            'agent_id': agent.id,
            'assignment_id': agent.assignment_id,
            'task_data': self.task_data,
            'response': self.response,
        }

        return save_data

    def shutdown(self):
        """
        Shutdown tracking for the agent.
        """
        self.mturk_agent.shutdown()