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]


PREHOOK: query: create table t1_n21 like src
PREHOOK: type: CREATETABLE
PREHOOK: Output: database:default
PREHOOK: Output: default@t1_n21
POSTHOOK: query: create table t1_n21 like src
POSTHOOK: type: CREATETABLE
POSTHOOK: Output: database:default
POSTHOOK: Output: default@t1_n21
PREHOOK: query: create table t2_n13 like src
PREHOOK: type: CREATETABLE
PREHOOK: Output: database:default
PREHOOK: Output: default@t2_n13
POSTHOOK: query: create table t2_n13 like src
POSTHOOK: type: CREATETABLE
POSTHOOK: Output: database:default
POSTHOOK: Output: default@t2_n13
PREHOOK: query: explain from src
insert into table t1_n21 select
key, GROUPING__ID
group by cube(key, value)
insert into table t2_n13 select
key, value
group by key, value grouping sets ((key), (key, value))
PREHOOK: type: QUERY
PREHOOK: Input: default@src
PREHOOK: Output: default@t1_n21
PREHOOK: Output: default@t2_n13
POSTHOOK: query: explain from src
insert into table t1_n21 select
key, GROUPING__ID
group by cube(key, value)
insert into table t2_n13 select
key, value
group by key, value grouping sets ((key), (key, value))
POSTHOOK: type: QUERY
POSTHOOK: Input: default@src
POSTHOOK: Output: default@t1_n21
POSTHOOK: Output: default@t2_n13
STAGE DEPENDENCIES:
  Stage-2 is a root stage
  Stage-3 depends on stages: Stage-2
  Stage-0 depends on stages: Stage-3
  Stage-4 depends on stages: Stage-0
  Stage-1 depends on stages: Stage-3
  Stage-5 depends on stages: Stage-1

STAGE PLANS:
  Stage: Stage-2
    Tez
#### A masked pattern was here ####
      Edges:
        Reducer 2 <- Map 1 (SIMPLE_EDGE)
        Reducer 3 <- Reducer 2 (CUSTOM_SIMPLE_EDGE)
        Reducer 4 <- Map 1 (SIMPLE_EDGE)
        Reducer 5 <- Reducer 4 (CUSTOM_SIMPLE_EDGE)
#### A masked pattern was here ####
      Vertices:
        Map 1 
            Map Operator Tree:
                TableScan
                  alias: src
                  Statistics: Num rows: 500 Data size: 89000 Basic stats: COMPLETE Column stats: COMPLETE
                  Select Operator
                    expressions: key (type: string), value (type: string)
                    outputColumnNames: key, value
                    Statistics: Num rows: 500 Data size: 89000 Basic stats: COMPLETE Column stats: COMPLETE
                    Group By Operator
                      keys: key (type: string), value (type: string), 0L (type: bigint)
                      minReductionHashAggr: 0.0
                      mode: hash
                      outputColumnNames: _col0, _col1, _col2
                      Statistics: Num rows: 1000 Data size: 186000 Basic stats: COMPLETE Column stats: COMPLETE
                      Reduce Output Operator
                        key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: bigint)
                        null sort order: zzz
                        sort order: +++
                        Map-reduce partition columns: _col0 (type: string), _col1 (type: string), _col2 (type: bigint)
                        Statistics: Num rows: 1000 Data size: 186000 Basic stats: COMPLETE Column stats: COMPLETE
                  Select Operator
                    expressions: key (type: string), value (type: string)
                    outputColumnNames: key, value
                    Statistics: Num rows: 500 Data size: 89000 Basic stats: COMPLETE Column stats: COMPLETE
                    Group By Operator
                      keys: key (type: string), value (type: string), 0L (type: bigint)
                      minReductionHashAggr: 0.0
                      mode: hash
                      outputColumnNames: _col0, _col1, _col2
                      Statistics: Num rows: 500 Data size: 93000 Basic stats: COMPLETE Column stats: COMPLETE
                      Reduce Output Operator
                        key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: bigint)
                        null sort order: zzz
                        sort order: +++
                        Map-reduce partition columns: _col0 (type: string), _col1 (type: string), _col2 (type: bigint)
                        Statistics: Num rows: 500 Data size: 93000 Basic stats: COMPLETE Column stats: COMPLETE
            Execution mode: vectorized, llap
            LLAP IO: all inputs
        Reducer 2 
            Execution mode: llap
            Reduce Operator Tree:
              Group By Operator
                keys: KEY._col0 (type: string), KEY._col1 (type: string), KEY._col2 (type: bigint)
                mode: mergepartial
                outputColumnNames: _col0, _col1, _col2
                Statistics: Num rows: 1000 Data size: 186000 Basic stats: COMPLETE Column stats: COMPLETE
                Select Operator
                  expressions: _col0 (type: string), CAST( _col2 AS STRING) (type: string)
                  outputColumnNames: _col0, _col1
                  Statistics: Num rows: 1000 Data size: 271000 Basic stats: COMPLETE Column stats: COMPLETE
                  File Output Operator
                    compressed: false
                    Statistics: Num rows: 1000 Data size: 271000 Basic stats: COMPLETE Column stats: COMPLETE
                    table:
                        input format: org.apache.hadoop.mapred.TextInputFormat
                        output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
                        serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
                        name: default.t1_n21
                  Select Operator
                    expressions: _col0 (type: string), _col1 (type: string)
                    outputColumnNames: key, value
                    Statistics: Num rows: 1000 Data size: 271000 Basic stats: COMPLETE Column stats: COMPLETE
                    Group By Operator
                      aggregations: max(length(key)), avg(COALESCE(length(key),0)), count(1), count(key), compute_bit_vector(key, 'hll'), max(length(value)), avg(COALESCE(length(value),0)), count(value), compute_bit_vector(value, 'hll')
                      minReductionHashAggr: 0.99
                      mode: hash
                      outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
                      Statistics: Num rows: 1 Data size: 472 Basic stats: COMPLETE Column stats: COMPLETE
                      Reduce Output Operator
                        null sort order: 
                        sort order: 
                        Statistics: Num rows: 1 Data size: 472 Basic stats: COMPLETE Column stats: COMPLETE
                        value expressions: _col0 (type: int), _col1 (type: struct<count:bigint,sum:double,input:int>), _col2 (type: bigint), _col3 (type: bigint), _col4 (type: binary), _col5 (type: int), _col6 (type: struct<count:bigint,sum:double,input:int>), _col7 (type: bigint), _col8 (type: binary)
        Reducer 3 
            Execution mode: llap
            Reduce Operator Tree:
              Group By Operator
                aggregations: max(VALUE._col0), avg(VALUE._col1), count(VALUE._col2), count(VALUE._col3), compute_bit_vector(VALUE._col4), max(VALUE._col5), avg(VALUE._col6), count(VALUE._col7), compute_bit_vector(VALUE._col8)
                mode: mergepartial
                outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
                Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: COMPLETE
                Select Operator
                  expressions: 'STRING' (type: string), UDFToLong(COALESCE(_col0,0)) (type: bigint), COALESCE(_col1,0) (type: double), (_col2 - _col3) (type: bigint), COALESCE(ndv_compute_bit_vector(_col4),0) (type: bigint), _col4 (type: binary), 'STRING' (type: string), UDFToLong(COALESCE(_col5,0)) (type: bigint), COALESCE(_col6,0) (type: double), (_col2 - _col7) (type: bigint), COALESCE(ndv_compute_bit_vector(_col8),0) (type: bigint), _col8 (type: binary)
                  outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11
                  Statistics: Num rows: 1 Data size: 532 Basic stats: COMPLETE Column stats: COMPLETE
                  File Output Operator
                    compressed: false
                    Statistics: Num rows: 1 Data size: 532 Basic stats: COMPLETE Column stats: COMPLETE
                    table:
                        input format: org.apache.hadoop.mapred.SequenceFileInputFormat
                        output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
                        serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
        Reducer 4 
            Execution mode: llap
            Reduce Operator Tree:
              Group By Operator
                keys: KEY._col0 (type: string), KEY._col1 (type: string), KEY._col2 (type: bigint)
                mode: mergepartial
                outputColumnNames: _col0, _col1
                Statistics: Num rows: 500 Data size: 93000 Basic stats: COMPLETE Column stats: COMPLETE
                pruneGroupingSetId: true
                File Output Operator
                  compressed: false
                  Statistics: Num rows: 500 Data size: 93000 Basic stats: COMPLETE Column stats: COMPLETE
                  table:
                      input format: org.apache.hadoop.mapred.TextInputFormat
                      output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
                      serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
                      name: default.t2_n13
                Select Operator
                  expressions: _col0 (type: string), _col1 (type: string)
                  outputColumnNames: key, value
                  Statistics: Num rows: 500 Data size: 89000 Basic stats: COMPLETE Column stats: COMPLETE
                  Group By Operator
                    aggregations: max(length(key)), avg(COALESCE(length(key),0)), count(1), count(key), compute_bit_vector(key, 'hll'), max(length(value)), avg(COALESCE(length(value),0)), count(value), compute_bit_vector(value, 'hll')
                    minReductionHashAggr: 0.99
                    mode: hash
                    outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
                    Statistics: Num rows: 1 Data size: 472 Basic stats: COMPLETE Column stats: COMPLETE
                    Reduce Output Operator
                      null sort order: 
                      sort order: 
                      Statistics: Num rows: 1 Data size: 472 Basic stats: COMPLETE Column stats: COMPLETE
                      value expressions: _col0 (type: int), _col1 (type: struct<count:bigint,sum:double,input:int>), _col2 (type: bigint), _col3 (type: bigint), _col4 (type: binary), _col5 (type: int), _col6 (type: struct<count:bigint,sum:double,input:int>), _col7 (type: bigint), _col8 (type: binary)
        Reducer 5 
            Execution mode: llap
            Reduce Operator Tree:
              Group By Operator
                aggregations: max(VALUE._col0), avg(VALUE._col1), count(VALUE._col2), count(VALUE._col3), compute_bit_vector(VALUE._col4), max(VALUE._col5), avg(VALUE._col6), count(VALUE._col7), compute_bit_vector(VALUE._col8)
                mode: mergepartial
                outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
                Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: COMPLETE
                Select Operator
                  expressions: 'STRING' (type: string), UDFToLong(COALESCE(_col0,0)) (type: bigint), COALESCE(_col1,0) (type: double), (_col2 - _col3) (type: bigint), COALESCE(ndv_compute_bit_vector(_col4),0) (type: bigint), _col4 (type: binary), 'STRING' (type: string), UDFToLong(COALESCE(_col5,0)) (type: bigint), COALESCE(_col6,0) (type: double), (_col2 - _col7) (type: bigint), COALESCE(ndv_compute_bit_vector(_col8),0) (type: bigint), _col8 (type: binary)
                  outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11
                  Statistics: Num rows: 1 Data size: 532 Basic stats: COMPLETE Column stats: COMPLETE
                  File Output Operator
                    compressed: false
                    Statistics: Num rows: 1 Data size: 532 Basic stats: COMPLETE Column stats: COMPLETE
                    table:
                        input format: org.apache.hadoop.mapred.SequenceFileInputFormat
                        output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
                        serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe

  Stage: Stage-3
    Dependency Collection

  Stage: Stage-0
    Move Operator
      tables:
          replace: false
          table:
              input format: org.apache.hadoop.mapred.TextInputFormat
              output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
              serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
              name: default.t1_n21

  Stage: Stage-4
    Stats Work
      Basic Stats Work:
      Column Stats Desc:
          Columns: key, value
          Column Types: string, string
          Table: default.t1_n21

  Stage: Stage-1
    Move Operator
      tables:
          replace: false
          table:
              input format: org.apache.hadoop.mapred.TextInputFormat
              output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
              serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
              name: default.t2_n13

  Stage: Stage-5
    Stats Work
      Basic Stats Work:
      Column Stats Desc:
          Columns: key, value
          Column Types: string, string
          Table: default.t2_n13